WP44: Part IV methodology (Agent L), discrepancy register, A-005

Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>

Simon-Pierre Boucher committed 5 h ago (Nov 3, 2026) parent eac709c Browse files

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added manuscript/references_part02.bib +2440 −0

++% references_part02.bib — WP44 Part II (Agent L). Every entry verified 2026-11-03 (see references_verification_part02.csv).
++
++@misc{priem2022openalex,
++  title = {{OpenAlex:} {A} fully-open index of scholarly works, authors, venues, institutions, and concepts},
++  author = {Jason Priem and Heather Piwowar and Richard Orr},
++  year = {2022},
++  eprint = {2205.01833},
++  archiveprefix = {arXiv},
++  howpublished = {arXiv preprint arXiv:2205.01833},
++  note = {verified: arxiv API 2026-11-03}
++}
++
++@misc{kinney2023s2,
++  title = {The {Semantic} {Scholar} {Open} {Data} {Platform}},
++  author = {Rodney Kinney and Chloe Anastasiades and Russell Authur and Iz Beltagy and Jonathan Bragg and Alexandra Buraczynski and Isabel Cachola and Stefan Candra and Yoganand Chandrasekhar and Arman Cohan and others},
++  year = {2023},
++  eprint = {2301.10140},
++  archiveprefix = {arXiv},
++  howpublished = {arXiv preprint arXiv:2301.10140},
++  note = {verified: arxiv API 2026-11-03}
++}
++
++@misc{culbert2025openalex,
++  title = {Reference {Coverage} {Analysis} of {OpenAlex} compared to {Web} of {Science} and {Scopus}},
++  author = {Jack Culbert and Anne Hobert and Najko Jahn and Nick Haupka and Marion Schmidt and Paul Donner and Philipp Mayr},
++  year = {2024},
++  eprint = {2401.16359},
++  archiveprefix = {arXiv},
++  howpublished = {arXiv preprint arXiv:2401.16359},
++  note = {verified: arxiv API 2026-11-03}
++}
++
++@misc{sutskever2014seq2seq,
++  title = {Sequence to {Sequence} {Learning} with {Neural} {Networks}},
++  author = {Ilya Sutskever and Oriol Vinyals and Quoc V. Le},
++  year = {2014},
++  eprint = {1409.3215},
++  archiveprefix = {arXiv},
++  howpublished = {arXiv preprint arXiv:1409.3215},
++  note = {verified: arxiv API 2026-11-03}
++}
++
++@misc{bahdanau2015attention,
++  title = {Neural {Machine} {Translation} by {Jointly} {Learning} to {Align} and {Translate}},
++  author = {Dzmitry Bahdanau and Kyunghyun Cho and Yoshua Bengio},
++  year = {2014},
++  eprint = {1409.0473},
++  archiveprefix = {arXiv},
++  howpublished = {arXiv preprint arXiv:1409.0473},
++  note = {verified: arxiv API 2026-11-03}
++}
++
++@misc{goodfellow2014gan,
++  title = {Generative {Adversarial} {Networks}},
++  author = {Ian J. Goodfellow and Jean Pouget-Abadie and Mehdi Mirza and Bing Xu and David Warde-Farley and Sherjil Ozair and Aaron Courville and Yoshua Bengio},
++  year = {2014},
++  eprint = {1406.2661},
++  archiveprefix = {arXiv},
++  howpublished = {arXiv preprint arXiv:1406.2661},
++  note = {verified: arxiv API 2026-11-03}
++}
++
++@misc{mikolov2013word2vec,
++  title = {Efficient {Estimation} of {Word} {Representations} in {Vector} {Space}},
++  author = {Tomas Mikolov and Kai Chen and Greg Corrado and Jeffrey Dean},
++  year = {2013},
++  eprint = {1301.3781},
++  archiveprefix = {arXiv},
++  howpublished = {arXiv preprint arXiv:1301.3781},
++  note = {verified: arxiv API 2026-11-03}
++}
++
++@misc{kingma2014vae,
++  title = {{Auto-Encoding} {Variational} {Bayes}},
++  author = {Diederik P Kingma and Max Welling},
++  year = {2013},
++  eprint = {1312.6114},
++  archiveprefix = {arXiv},
++  howpublished = {arXiv preprint arXiv:1312.6114},
++  note = {verified: arxiv API 2026-11-03}
++}
++
++@misc{sevilla2022compute,
++  title = {Compute {Trends} {Across} {Three} {Eras} of {Machine} {Learning}},
++  author = {Jaime Sevilla and Lennart Heim and Anson Ho and Tamay Besiroglu and Marius Hobbhahn and Pablo Villalobos},
++  year = {2022},
++  eprint = {2202.05924},
++  archiveprefix = {arXiv},
++  howpublished = {arXiv preprint arXiv:2202.05924},
++  note = {verified: arxiv API 2026-11-03}
++}
++
++@misc{thompson2020limits,
++  title = {The {Computational} {Limits} of {Deep} {Learning}},
++  author = {Neil C. Thompson and Kristjan Greenewald and Keeheon Lee and Gabriel F. Manso},
++  year = {2020},
++  eprint = {2007.05558},
++  archiveprefix = {arXiv},
++  howpublished = {arXiv preprint arXiv:2007.05558},
++  note = {verified: arxiv API 2026-11-03}
++}
++
++@misc{ahmed2020dedemocratization,
++  title = {The {De-democratization} of {AI:} {Deep} {Learning} and the {Compute} {Divide} in {Artificial} {Intelligence} {Research}},
++  author = {Nur Ahmed and Muntasir Wahed},
++  year = {2020},
++  eprint = {2010.15581},
++  archiveprefix = {arXiv},
++  howpublished = {arXiv preprint arXiv:2010.15581},
++  note = {verified: arxiv API 2026-11-03}
++}
++
++@misc{abadi2016tensorflow,
++  title = {{TensorFlow:} {A} system for large-scale machine learning},
++  author = {Martín Abadi and Paul Barham and Jianmin Chen and Zhifeng Chen and Andy Davis and Jeffrey Dean and Matthieu Devin and Sanjay Ghemawat and Geoffrey Irving and Michael Isard and others},
++  year = {2016},
++  eprint = {1605.08695},
++  archiveprefix = {arXiv},
++  howpublished = {arXiv preprint arXiv:1605.08695},
++  note = {verified: arxiv API 2026-11-03}
++}
++
++@misc{paszke2019pytorch,
++  title = {{PyTorch:} {An} {Imperative} {Style,} {High-Performance} {Deep} {Learning} {Library}},
++  author = {Adam Paszke and Sam Gross and Francisco Massa and Adam Lerer and James Bradbury and Gregory Chanan and Trevor Killeen and Zeming Lin and Natalia Gimelshein and Luca Antiga and others},
++  year = {2019},
++  eprint = {1912.01703},
++  archiveprefix = {arXiv},
++  howpublished = {arXiv preprint arXiv:1912.01703},
++  note = {verified: arxiv API 2026-11-03}
++}
++
++@misc{vaswani2017attention,
++  title = {Attention {Is} {All} {You} {Need}},
++  author = {Ashish Vaswani and Noam Shazeer and Niki Parmar and Jakob Uszkoreit and Llion Jones and Aidan N. Gomez and Lukasz Kaiser and Illia Polosukhin},
++  year = {2017},
++  eprint = {1706.03762},
++  archiveprefix = {arXiv},
++  howpublished = {arXiv preprint arXiv:1706.03762},
++  note = {verified: arxiv API 2026-11-03}
++}
++
++@misc{brown2020gpt3,
++  title = {Language {Models} are {Few-Shot} {Learners}},
++  author = {Tom B. Brown and Benjamin Mann and Nick Ryder and Melanie Subbiah and Jared Kaplan and Prafulla Dhariwal and Arvind Neelakantan and Pranav Shyam and Girish Sastry and Amanda Askell and others},
++  year = {2020},
++  eprint = {2005.14165},
++  archiveprefix = {arXiv},
++  howpublished = {arXiv preprint arXiv:2005.14165},
++  note = {verified: arxiv API 2026-11-03}
++}
++
++@misc{raffel2020t5,
++  title = {Exploring the {Limits} of {Transfer} {Learning} with a {Unified} {Text-to-Text} {Transformer}},
++  author = {Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu},
++  year = {2019},
++  eprint = {1910.10683},
++  archiveprefix = {arXiv},
++  howpublished = {arXiv preprint arXiv:1910.10683},
++  note = {verified: arxiv API 2026-11-03}
++}
++
++@misc{kaplan2020scaling,
++  title = {Scaling {Laws} for {Neural} {Language} {Models}},
++  author = {Jared Kaplan and Sam McCandlish and Tom Henighan and Tom B. Brown and Benjamin Chess and Rewon Child and Scott Gray and Alec Radford and Jeffrey Wu and Dario Amodei},
++  year = {2020},
++  eprint = {2001.08361},
++  archiveprefix = {arXiv},
++  howpublished = {arXiv preprint arXiv:2001.08361},
++  note = {verified: arxiv API 2026-11-03}
++}
++
++@misc{hoffmann2022chinchilla,
++  title = {Training {Compute-Optimal} {Large} {Language} {Models}},
++  author = {Jordan Hoffmann and Sebastian Borgeaud and Arthur Mensch and Elena Buchatskaya and Trevor Cai and Eliza Rutherford and Diego de Las Casas and Lisa Anne Hendricks and Johannes Welbl and Aidan Clark and others},
++  year = {2022},
++  eprint = {2203.15556},
++  archiveprefix = {arXiv},
++  howpublished = {arXiv preprint arXiv:2203.15556},
++  note = {verified: arxiv API 2026-11-03}
++}
++
++@misc{chowdhery2022palm,
++  title = {{PaLM:} {Scaling} {Language} {Modeling} with {Pathways}},
++  author = {Aakanksha Chowdhery and Sharan Narang and Jacob Devlin and Maarten Bosma and Gaurav Mishra and Adam Roberts and Paul Barham and Hyung Won Chung and Charles Sutton and Sebastian Gehrmann and others},
++  year = {2022},
++  eprint = {2204.02311},
++  archiveprefix = {arXiv},
++  howpublished = {arXiv preprint arXiv:2204.02311},
++  note = {verified: arxiv API 2026-11-03}
++}
++
++@misc{fedus2022switch,
++  title = {Switch {Transformers:} {Scaling} to {Trillion} {Parameter} {Models} with {Simple} and {Efficient} {Sparsity}},
++  author = {William Fedus and Barret Zoph and Noam Shazeer},
++  year = {2021},
++  eprint = {2101.03961},
++  archiveprefix = {arXiv},
++  howpublished = {arXiv preprint arXiv:2101.03961},
++  note = {verified: arxiv API 2026-11-03}
++}
++
++@misc{dosovitskiy2021vit,
++  title = {An {Image} is {Worth} 16x16 {Words:} {Transformers} for {Image} {Recognition} at {Scale}},
++  author = {Alexey Dosovitskiy and Lucas Beyer and Alexander Kolesnikov and Dirk Weissenborn and Xiaohua Zhai and Thomas Unterthiner and Mostafa Dehghani and Matthias Minderer and Georg Heigold and Sylvain Gelly and Jakob Uszkoreit and Neil Houlsby},
++  year = {2020},
++  eprint = {2010.11929},
++  archiveprefix = {arXiv},
++  howpublished = {arXiv preprint arXiv:2010.11929},
++  note = {verified: arxiv API 2026-11-03}
++}
++
++@misc{radford2021clip,
++  title = {Learning {Transferable} {Visual} {Models} {From} {Natural} {Language} {Supervision}},
++  author = {Alec Radford and Jong Wook Kim and Chris Hallacy and Aditya Ramesh and Gabriel Goh and Sandhini Agarwal and Girish Sastry and Amanda Askell and Pamela Mishkin and Jack Clark and Gretchen Krueger and Ilya Sutskever},
++  year = {2021},
++  eprint = {2103.00020},
++  archiveprefix = {arXiv},
++  howpublished = {arXiv preprint arXiv:2103.00020},
++  note = {verified: arxiv API 2026-11-03}
++}
++
++@misc{ramesh2021dalle,
++  title = {{Zero-Shot} {Text-to-Image} {Generation}},
++  author = {Aditya Ramesh and Mikhail Pavlov and Gabriel Goh and Scott Gray and Chelsea Voss and Alec Radford and Mark Chen and Ilya Sutskever},
++  year = {2021},
++  eprint = {2102.12092},
++  archiveprefix = {arXiv},
++  howpublished = {arXiv preprint arXiv:2102.12092},
++  note = {verified: arxiv API 2026-11-03}
++}
++
++@misc{ho2020ddpm,
++  title = {Denoising {Diffusion} {Probabilistic} {Models}},
++  author = {Jonathan Ho and Ajay Jain and Pieter Abbeel},
++  year = {2020},
++  eprint = {2006.11239},
++  archiveprefix = {arXiv},
++  howpublished = {arXiv preprint arXiv:2006.11239},
++  note = {verified: arxiv API 2026-11-03}
++}
++
++@misc{sohl2015diffusion,
++  title = {Deep {Unsupervised} {Learning} using {Nonequilibrium} {Thermodynamics}},
++  author = {Jascha Sohl-Dickstein and Eric A. Weiss and Niru Maheswaranathan and Surya Ganguli},
++  year = {2015},
++  eprint = {1503.03585},
++  archiveprefix = {arXiv},
++  howpublished = {arXiv preprint arXiv:1503.03585},
++  note = {verified: arxiv API 2026-11-03}
++}
++
++@misc{song2021score,
++  title = {{Score-Based} {Generative} {Modeling} through {Stochastic} {Differential} {Equations}},
++  author = {Yang Song and Jascha Sohl-Dickstein and Diederik P. Kingma and Abhishek Kumar and Stefano Ermon and Ben Poole},
++  year = {2020},
++  eprint = {2011.13456},
++  archiveprefix = {arXiv},
++  howpublished = {arXiv preprint arXiv:2011.13456},
++  note = {verified: arxiv API 2026-11-03}
++}
++
++@misc{dhariwal2021beats,
++  title = {Diffusion {Models} {Beat} {GANs} on {Image} {Synthesis}},
++  author = {Prafulla Dhariwal and Alex Nichol},
++  year = {2021},
++  eprint = {2105.05233},
++  archiveprefix = {arXiv},
++  howpublished = {arXiv preprint arXiv:2105.05233},
++  note = {verified: arxiv API 2026-11-03}
++}
++
++@misc{bommasani2021foundation,
++  title = {On the {Opportunities} and {Risks} of {Foundation} {Models}},
++  author = {Rishi Bommasani and Drew A. Hudson and Ehsan Adeli and Russ Altman and Simran Arora and Sydney von Arx and Michael S. Bernstein and Jeannette Bohg and Antoine Bosselut and Emma Brunskill and others},
++  year = {2021},
++  eprint = {2108.07258},
++  archiveprefix = {arXiv},
++  howpublished = {arXiv preprint arXiv:2108.07258},
++  note = {verified: arxiv API 2026-11-03}
++}
++
++@misc{wei2022cot,
++  title = {{Chain-of-Thought} {Prompting} {Elicits} {Reasoning} in {Large} {Language} {Models}},
++  author = {Jason Wei and Xuezhi Wang and Dale Schuurmans and Maarten Bosma and Brian Ichter and Fei Xia and Ed Chi and Quoc Le and Denny Zhou},
++  year = {2022},
++  eprint = {2201.11903},
++  archiveprefix = {arXiv},
++  howpublished = {arXiv preprint arXiv:2201.11903},
++  note = {verified: arxiv API 2026-11-03}
++}
++
++@misc{wei2022emergent,
++  title = {Emergent {Abilities} of {Large} {Language} {Models}},
++  author = {Jason Wei and Yi Tay and Rishi Bommasani and Colin Raffel and Barret Zoph and Sebastian Borgeaud and Dani Yogatama and Maarten Bosma and Denny Zhou and Donald Metzler and others},
++  year = {2022},
++  eprint = {2206.07682},
++  archiveprefix = {arXiv},
++  howpublished = {arXiv preprint arXiv:2206.07682},
++  note = {verified: arxiv API 2026-11-03}
++}
++
++@misc{chen2021codex,
++  title = {Evaluating {Large} {Language} {Models} {Trained} on {Code}},
++  author = {Mark Chen and Jerry Tworek and Heewoo Jun and Qiming Yuan and Henrique Ponde de Oliveira Pinto and Jared Kaplan and Harri Edwards and Yuri Burda and Nicholas Joseph and Greg Brockman and others},
++  year = {2021},
++  eprint = {2107.03374},
++  archiveprefix = {arXiv},
++  howpublished = {arXiv preprint arXiv:2107.03374},
++  note = {verified: arxiv API 2026-11-03}
++}
++
++@misc{ouyang2022instructgpt,
++  title = {Training language models to follow instructions with human feedback},
++  author = {Long Ouyang and Jeff Wu and Xu Jiang and Diogo Almeida and Carroll L. Wainwright and Pamela Mishkin and Chong Zhang and Sandhini Agarwal and Katarina Slama and Alex Ray and others},
++  year = {2022},
++  eprint = {2203.02155},
++  archiveprefix = {arXiv},
++  howpublished = {arXiv preprint arXiv:2203.02155},
++  note = {verified: arxiv API 2026-11-03}
++}
++
++@misc{christiano2017rlhf,
++  title = {Deep reinforcement learning from human preferences},
++  author = {Paul Christiano and Jan Leike and Tom B. Brown and Miljan Martic and Shane Legg and Dario Amodei},
++  year = {2017},
++  eprint = {1706.03741},
++  archiveprefix = {arXiv},
++  howpublished = {arXiv preprint arXiv:1706.03741},
++  note = {verified: arxiv API 2026-11-03}
++}
++
++@misc{stiennon2020summarize,
++  title = {Learning to summarize from human feedback},
++  author = {Nisan Stiennon and Long Ouyang and Jeff Wu and Daniel M. Ziegler and Ryan Lowe and Chelsea Voss and Alec Radford and Dario Amodei and Paul Christiano},
++  year = {2020},
++  eprint = {2009.01325},
++  archiveprefix = {arXiv},
++  howpublished = {arXiv preprint arXiv:2009.01325},
++  note = {verified: arxiv API 2026-11-03}
++}
++
++@misc{bai2022hh,
++  title = {Training a {Helpful} and {Harmless} {Assistant} with {Reinforcement} {Learning} from {Human} {Feedback}},
++  author = {Yuntao Bai and Andy Jones and Kamal Ndousse and Amanda Askell and Anna Chen and Nova DasSarma and Dawn Drain and Stanislav Fort and Deep Ganguli and Tom Henighan and others},
++  year = {2022},
++  eprint = {2204.05862},
++  archiveprefix = {arXiv},
++  howpublished = {arXiv preprint arXiv:2204.05862},
++  note = {verified: arxiv API 2026-11-03}
++}
++
++@misc{lewis2020rag,
++  title = {{Retrieval-Augmented} {Generation} for {Knowledge-Intensive} {NLP} {Tasks}},
++  author = {Patrick Lewis and Ethan Perez and Aleksandra Piktus and Fabio Petroni and Vladimir Karpukhin and Naman Goyal and Heinrich Küttler and Mike Lewis and Wen-tau Yih and Tim Rocktäschel and Sebastian Riedel and Douwe Kiela},
++  year = {2020},
++  eprint = {2005.11401},
++  archiveprefix = {arXiv},
++  howpublished = {arXiv preprint arXiv:2005.11401},
++  note = {verified: arxiv API 2026-11-03}
++}
++
++@misc{hendrycks2021mmlu,
++  title = {Measuring {Massive} {Multitask} {Language} {Understanding}},
++  author = {Dan Hendrycks and Collin Burns and Steven Basart and Andy Zou and Mantas Mazeika and Dawn Song and Jacob Steinhardt},
++  year = {2020},
++  eprint = {2009.03300},
++  archiveprefix = {arXiv},
++  howpublished = {arXiv preprint arXiv:2009.03300},
++  note = {verified: arxiv API 2026-11-03}
++}
++
++@misc{wang2019superglue,
++  title = {{SuperGLUE:} {A} {Stickier} {Benchmark} for {General-Purpose} {Language} {Understanding} {Systems}},
++  author = {Alex Wang and Yada Pruksachatkun and Nikita Nangia and Amanpreet Singh and Julian Michael and Felix Hill and Omer Levy and Samuel R. Bowman},
++  year = {2019},
++  eprint = {1905.00537},
++  archiveprefix = {arXiv},
++  howpublished = {arXiv preprint arXiv:1905.00537},
++  note = {verified: arxiv API 2026-11-03}
++}
++
++@misc{srivastava2023bigbench,
++  title = {Beyond the {Imitation} {Game:} {Quantifying} and extrapolating the capabilities of language models},
++  author = {Aarohi Srivastava and Abhinav Rastogi and Abhishek Rao and Abu Awal Md Shoeb and Abubakar Abid and Adam Fisch and Adam R. Brown and Adam Santoro and Aditya Gupta and Adrià Garriga-Alonso and others},
++  year = {2022},
++  eprint = {2206.04615},
++  archiveprefix = {arXiv},
++  howpublished = {arXiv preprint arXiv:2206.04615},
++  note = {verified: arxiv API 2026-11-03}
++}
++
++@misc{patterson2021carbon,
++  title = {Carbon {Emissions} and {Large} {Neural} {Network} {Training}},
++  author = {David Patterson and Joseph Gonzalez and Quoc Le and Chen Liang and Lluis-Miquel Munguia and Daniel Rothchild and David So and Maud Texier and Jeff Dean},
++  year = {2021},
++  eprint = {2104.10350},
++  archiveprefix = {arXiv},
++  howpublished = {arXiv preprint arXiv:2104.10350},
++  note = {verified: arxiv API 2026-11-03}
++}
++
++@misc{shoeybi2019megatron,
++  title = {{Megatron-LM:} {Training} {Multi-Billion} {Parameter} {Language} {Models} {Using} {Model} {Parallelism}},
++  author = {Mohammad Shoeybi and Mostofa Patwary and Raul Puri and Patrick LeGresley and Jared Casper and Bryan Catanzaro},
++  year = {2019},
++  eprint = {1909.08053},
++  archiveprefix = {arXiv},
++  howpublished = {arXiv preprint arXiv:1909.08053},
++  note = {verified: arxiv API 2026-11-03}
++}
++
++@misc{rajbhandari2020zero,
++  title = {{ZeRO:} {Memory} {Optimizations} {Toward} {Training} {Trillion} {Parameter} {Models}},
++  author = {Samyam Rajbhandari and Jeff Rasley and Olatunji Ruwase and Yuxiong He},
++  year = {2019},
++  eprint = {1910.02054},
++  archiveprefix = {arXiv},
++  howpublished = {arXiv preprint arXiv:1910.02054},
++  note = {verified: arxiv API 2026-11-03}
++}
++
++@misc{dao2022flashattention,
++  title = {{FlashAttention:} {Fast} and {Memory-Efficient} {Exact} {Attention} with {IO-Awareness}},
++  author = {Tri Dao and Daniel Y. Fu and Stefano Ermon and Atri Rudra and Christopher Ré},
++  year = {2022},
++  eprint = {2205.14135},
++  archiveprefix = {arXiv},
++  howpublished = {arXiv preprint arXiv:2205.14135},
++  note = {verified: arxiv API 2026-11-03}
++}
++
++@misc{rae2021gopher,
++  title = {Scaling {Language} {Models:} {Methods,} {Analysis} \& {Insights} from {Training} {Gopher}},
++  author = {Jack W. Rae and Sebastian Borgeaud and Trevor Cai and Katie Millican and Jordan Hoffmann and Francis Song and John Aslanides and Sarah Henderson and Roman Ring and Susannah Young and others},
++  year = {2021},
++  eprint = {2112.11446},
++  archiveprefix = {arXiv},
++  howpublished = {arXiv preprint arXiv:2112.11446},
++  note = {verified: arxiv API 2026-11-03}
++}
++
++@misc{bigscience2022bloom,
++  title = {{BLOOM:} {A} {176B-Parameter} {Open-Access} {Multilingual} {Language} {Model}},
++  author = {{BigScience Workshop}},
++  year = {2022},
++  eprint = {2211.05100},
++  archiveprefix = {arXiv},
++  howpublished = {arXiv preprint arXiv:2211.05100},
++  note = {verified: arxiv API 2026-11-03}
++}
++
++@misc{zhang2022opt,
++  title = {{OPT:} {Open} {Pre-trained} {Transformer} {Language} {Models}},
++  author = {Susan Zhang and Stephen Roller and Naman Goyal and Mikel Artetxe and Moya Chen and Shuohui Chen and Christopher Dewan and Mona Diab and Xian Li and Xi Victoria Lin and others},
++  year = {2022},
++  eprint = {2205.01068},
++  archiveprefix = {arXiv},
++  howpublished = {arXiv preprint arXiv:2205.01068},
++  note = {verified: arxiv API 2026-11-03}
++}
++
++@misc{liu2019roberta,
++  title = {{RoBERTa:} {A} {Robustly} {Optimized} {BERT} {Pretraining} {Approach}},
++  author = {Yinhan Liu and Myle Ott and Naman Goyal and Jingfei Du and Mandar Joshi and Danqi Chen and Omer Levy and Mike Lewis and Luke Zettlemoyer and Veselin Stoyanov},
++  year = {2019},
++  eprint = {1907.11692},
++  archiveprefix = {arXiv},
++  howpublished = {arXiv preprint arXiv:1907.11692},
++  note = {verified: arxiv API 2026-11-03}
++}
++
++@misc{chen2020simclr,
++  title = {{A} {Simple} {Framework} for {Contrastive} {Learning} of {Visual} {Representations}},
++  author = {Ting Chen and Simon Kornblith and Mohammad Norouzi and Geoffrey Hinton},
++  year = {2020},
++  eprint = {2002.05709},
++  archiveprefix = {arXiv},
++  howpublished = {arXiv preprint arXiv:2002.05709},
++  note = {verified: arxiv API 2026-11-03}
++}
++
++@misc{baevski2020wav2vec,
++  title = {wav2vec 2.0: {A} {Framework} for {Self-Supervised} {Learning} of {Speech} {Representations}},
++  author = {Alexei Baevski and Henry Zhou and Abdelrahman Mohamed and Michael Auli},
++  year = {2020},
++  eprint = {2006.11477},
++  archiveprefix = {arXiv},
++  howpublished = {arXiv preprint arXiv:2006.11477},
++  note = {verified: arxiv API 2026-11-03}
++}
++
++@misc{alayrac2022flamingo,
++  title = {Flamingo: a {Visual} {Language} {Model} for {Few-Shot} {Learning}},
++  author = {Jean-Baptiste Alayrac and Jeff Donahue and Pauline Luc and Antoine Miech and Iain Barr and Yana Hasson and Karel Lenc and Arthur Mensch and Katie Millican and Malcolm Reynolds and others},
++  year = {2022},
++  eprint = {2204.14198},
++  archiveprefix = {arXiv},
++  howpublished = {arXiv preprint arXiv:2204.14198},
++  note = {verified: arxiv API 2026-11-03}
++}
++
++@misc{reed2022gato,
++  title = {{A} {Generalist} {Agent}},
++  author = {Scott Reed and Konrad Zolna and Emilio Parisotto and Sergio Gomez Colmenarejo and Alexander Novikov and Gabriel Barth-Maron and Mai Gimenez and Yury Sulsky and Jackie Kay and Jost Tobias Springenberg and others},
++  year = {2022},
++  eprint = {2205.06175},
++  archiveprefix = {arXiv},
++  howpublished = {arXiv preprint arXiv:2205.06175},
++  note = {verified: arxiv API 2026-11-03}
++}
++
++@misc{saharia2022imagen,
++  title = {Photorealistic {Text-to-Image} {Diffusion} {Models} with {Deep} {Language} {Understanding}},
++  author = {Chitwan Saharia and William Chan and Saurabh Saxena and Lala Li and Jay Whang and Emily Denton and Seyed Kamyar Seyed Ghasemipour and Burcu Karagol Ayan and S. Sara Mahdavi and Rapha Gontijo Lopes and others},
++  year = {2022},
++  eprint = {2205.11487},
++  archiveprefix = {arXiv},
++  howpublished = {arXiv preprint arXiv:2205.11487},
++  note = {verified: arxiv API 2026-11-03}
++}
++
++@misc{ramesh2022dalle2,
++  title = {Hierarchical {Text-Conditional} {Image} {Generation} with {CLIP} {Latents}},
++  author = {Aditya Ramesh and Prafulla Dhariwal and Alex Nichol and Casey Chu and Mark Chen},
++  year = {2022},
++  eprint = {2204.06125},
++  archiveprefix = {arXiv},
++  howpublished = {arXiv preprint arXiv:2204.06125},
++  note = {verified: arxiv API 2026-11-03}
++}
++
++@misc{jonas2019serverless,
++  title = {Cloud {Programming} {Simplified:} {A} {Berkeley} {View} on {Serverless} {Computing}},
++  author = {Eric Jonas and Johann Schleier-Smith and Vikram Sreekanti and Chia-Che Tsai and Anurag Khandelwal and Qifan Pu and Vaishaal Shankar and Joao Carreira and Karl Krauth and Neeraja Yadwadkar and others},
++  year = {2019},
++  eprint = {1902.03383},
++  archiveprefix = {arXiv},
++  howpublished = {arXiv preprint arXiv:1902.03383},
++  note = {verified: arxiv API 2026-11-03}
++}
++
++@misc{hellerstein2019serverless,
++  title = {Serverless {Computing:} {One} {Step} {Forward,} {Two} {Steps} {Back}},
++  author = {Joseph M. Hellerstein and Jose Faleiro and Joseph E. Gonzalez and Johann Schleier-Smith and Vikram Sreekanti and Alexey Tumanov and Chenggang Wu},
++  year = {2018},
++  eprint = {1812.03651},
++  archiveprefix = {arXiv},
++  howpublished = {arXiv preprint arXiv:1812.03651},
++  note = {verified: arxiv API 2026-11-03}
++}
++
++@misc{chen2018tvm,
++  title = {{TVM:} {An} {Automated} {End-to-End} {Optimizing} {Compiler} for {Deep} {Learning}},
++  author = {Tianqi Chen and Thierry Moreau and Ziheng Jiang and Lianmin Zheng and Eddie Yan and Meghan Cowan and Haichen Shen and Leyuan Wang and Yuwei Hu and Luis Ceze and Carlos Guestrin and Arvind Krishnamurthy},
++  year = {2018},
++  eprint = {1802.04799},
++  archiveprefix = {arXiv},
++  howpublished = {arXiv preprint arXiv:1802.04799},
++  note = {verified: arxiv API 2026-11-03}
++}
++
++@misc{jacot2018ntk,
++  title = {Neural {Tangent} {Kernel:} {Convergence} and {Generalization} in {Neural} {Networks}},
++  author = {Arthur Jacot and Franck Gabriel and Clément Hongler},
++  year = {2018},
++  eprint = {1806.07572},
++  archiveprefix = {arXiv},
++  howpublished = {arXiv preprint arXiv:1806.07572},
++  note = {verified: arxiv API 2026-11-03}
++}
++
++@misc{lu2021codexglue,
++  title = {{CodeXGLUE:} {A} {Machine} {Learning} {Benchmark} {Dataset} for {Code} {Understanding} and {Generation}},
++  author = {Shuai Lu and Daya Guo and Shuo Ren and Junjie Huang and Alexey Svyatkovskiy and Ambrosio Blanco and Colin Clement and Dawn Drain and Daxin Jiang and Duyu Tang and others},
++  year = {2021},
++  eprint = {2102.04664},
++  archiveprefix = {arXiv},
++  howpublished = {arXiv preprint arXiv:2102.04664},
++  note = {verified: arxiv API 2026-11-03}
++}
++
++@misc{szegedy2014intriguing,
++  title = {Intriguing properties of neural networks},
++  author = {Christian Szegedy and Wojciech Zaremba and Ilya Sutskever and Joan Bruna and Dumitru Erhan and Ian Goodfellow and Rob Fergus},
++  year = {2013},
++  eprint = {1312.6199},
++  archiveprefix = {arXiv},
++  howpublished = {arXiv preprint arXiv:1312.6199},
++  note = {verified: arxiv API 2026-11-03}
++}
++
++@misc{mcmahan2017fl,
++  title = {{Communication-Efficient} {Learning} of {Deep} {Networks} from {Decentralized} {Data}},
++  author = {H. Brendan McMahan and Eider Moore and Daniel Ramage and Seth Hampson and Blaise Agüera y Arcas},
++  year = {2016},
++  eprint = {1602.05629},
++  archiveprefix = {arXiv},
++  howpublished = {arXiv preprint arXiv:1602.05629},
++  note = {verified: arxiv API 2026-11-03}
++}
++
++@misc{li2021fno,
++  title = {Fourier {Neural} {Operator} for {Parametric} {Partial} {Differential} {Equations}},
++  author = {Zongyi Li and Nikola Kovachki and Kamyar Azizzadenesheli and Burigede Liu and Kaushik Bhattacharya and Andrew Stuart and Anima Anandkumar},
++  year = {2020},
++  eprint = {2010.08895},
++  archiveprefix = {arXiv},
++  howpublished = {arXiv preprint arXiv:2010.08895},
++  note = {verified: arxiv API 2026-11-03}
++}
++
++@misc{luccioni2023bloom,
++  title = {Estimating the {Carbon} {Footprint} of {BLOOM,} a {176B} {Parameter} {Language} {Model}},
++  author = {Alexandra Sasha Luccioni and Sylvain Viguier and Anne-Laure Ligozat},
++  year = {2022},
++  eprint = {2211.02001},
++  archiveprefix = {arXiv},
++  howpublished = {arXiv preprint arXiv:2211.02001},
++  note = {verified: arxiv API 2026-11-03}
++}
++
++@misc{levine2016visuomotor,
++  title = {{End-to-End} {Training} of {Deep} {Visuomotor} {Policies}},
++  author = {Sergey Levine and Chelsea Finn and Trevor Darrell and Pieter Abbeel},
++  year = {2015},
++  eprint = {1504.00702},
++  archiveprefix = {arXiv},
++  howpublished = {arXiv preprint arXiv:1504.00702},
++  note = {verified: arxiv API 2026-11-03}
++}
++
++@misc{openai2023gpt4,
++  title = {{GPT-4} {Technical} {Report}},
++  author = {{OpenAI}},
++  year = {2023},
++  eprint = {2303.08774},
++  archiveprefix = {arXiv},
++  howpublished = {arXiv preprint arXiv:2303.08774},
++  note = {verified: arxiv API 2026-11-03}
++}
++
++@misc{touvron2023llama,
++  title = {{LLaMA:} {Open} and {Efficient} {Foundation} {Language} {Models}},
++  author = {Hugo Touvron and Thibaut Lavril and Gautier Izacard and Xavier Martinet and Marie-Anne Lachaux and Timothée Lacroix and Baptiste Rozière and Naman Goyal and Eric Hambro and Faisal Azhar and others},
++  year = {2023},
++  eprint = {2302.13971},
++  archiveprefix = {arXiv},
++  howpublished = {arXiv preprint arXiv:2302.13971},
++  note = {verified: arxiv API 2026-11-03}
++}
++
++@misc{touvron2023llama2,
++  title = {Llama 2: {Open} {Foundation} and {Fine-Tuned} {Chat} {Models}},
++  author = {Hugo Touvron and Louis Martin and Kevin Stone and Peter Albert and Amjad Almahairi and Yasmine Babaei and Nikolay Bashlykov and Soumya Batra and Prajjwal Bhargava and Shruti Bhosale and others},
++  year = {2023},
++  eprint = {2307.09288},
++  archiveprefix = {arXiv},
++  howpublished = {arXiv preprint arXiv:2307.09288},
++  note = {verified: arxiv API 2026-11-03}
++}
++
++@misc{grattafiori2024llama3,
++  title = {The {Llama} 3 {Herd} of {Models}},
++  author = {Aaron Grattafiori and Abhimanyu Dubey and Abhinav Jauhri and Abhinav Pandey and Abhishek Kadian and Ahmad Al-Dahle and Aiesha Letman and Akhil Mathur and Alan Schelten and Alex Vaughan and others},
++  year = {2024},
++  eprint = {2407.21783},
++  archiveprefix = {arXiv},
++  howpublished = {arXiv preprint arXiv:2407.21783},
++  note = {verified: arxiv API 2026-11-03}
++}
++
++@misc{bubeck2023sparks,
++  title = {Sparks of {Artificial} {General} {Intelligence:} {Early} experiments with {GPT-4}},
++  author = {Sébastien Bubeck and Varun Chandrasekaran and Ronen Eldan and Johannes Gehrke and Eric Horvitz and Ece Kamar and Peter Lee and Yin Tat Lee and Yuanzhi Li and Scott Lundberg and others},
++  year = {2023},
++  eprint = {2303.12712},
++  archiveprefix = {arXiv},
++  howpublished = {arXiv preprint arXiv:2303.12712},
++  note = {verified: arxiv API 2026-11-03}
++}
++
++@misc{openai2024o1,
++  title = {{OpenAI} o1 {System} {Card}},
++  author = {{OpenAI}},
++  year = {2024},
++  eprint = {2412.16720},
++  archiveprefix = {arXiv},
++  howpublished = {arXiv preprint arXiv:2412.16720},
++  note = {verified: arxiv API 2026-11-03}
++}
++
++@misc{deepseek2025r1,
++  title = {{DeepSeek-R1:} {Incentivizing} {Reasoning} {Capability} in {LLMs} via {Reinforcement} {Learning}},
++  author = {{DeepSeek-AI}},
++  year = {2025},
++  eprint = {2501.12948},
++  archiveprefix = {arXiv},
++  howpublished = {arXiv preprint arXiv:2501.12948},
++  note = {verified: arxiv API 2026-11-03}
++}
++
++@misc{gu2023mamba,
++  title = {Mamba: {Linear-Time} {Sequence} {Modeling} with {Selective} {State} {Spaces}},
++  author = {Albert Gu and Tri Dao},
++  year = {2023},
++  eprint = {2312.00752},
++  archiveprefix = {arXiv},
++  howpublished = {arXiv preprint arXiv:2312.00752},
++  note = {verified: arxiv API 2026-11-03}
++}
++
++@misc{yao2023react,
++  title = {{ReAct:} {Synergizing} {Reasoning} and {Acting} in {Language} {Models}},
++  author = {Shunyu Yao and Jeffrey Zhao and Dian Yu and Nan Du and Izhak Shafran and Karthik Narasimhan and Yuan Cao},
++  year = {2022},
++  eprint = {2210.03629},
++  archiveprefix = {arXiv},
++  howpublished = {arXiv preprint arXiv:2210.03629},
++  note = {verified: arxiv API 2026-11-03}
++}
++
++@misc{schick2023toolformer,
++  title = {Toolformer: {Language} {Models} {Can} {Teach} {Themselves} to {Use} {Tools}},
++  author = {Timo Schick and Jane Dwivedi-Yu and Roberto Dessì and Roberta Raileanu and Maria Lomeli and Luke Zettlemoyer and Nicola Cancedda and Thomas Scialom},
++  year = {2023},
++  eprint = {2302.04761},
++  archiveprefix = {arXiv},
++  howpublished = {arXiv preprint arXiv:2302.04761},
++  note = {verified: arxiv API 2026-11-03}
++}
++
++@misc{gemini2023,
++  title = {Gemini: {A} {Family} of {Highly} {Capable} {Multimodal} {Models}},
++  author = {{Gemini Team, Google}},
++  year = {2023},
++  eprint = {2312.11805},
++  archiveprefix = {arXiv},
++  howpublished = {arXiv preprint arXiv:2312.11805},
++  note = {verified: arxiv API 2026-11-03}
++}
++
++@misc{radford2023whisper,
++  title = {Robust {Speech} {Recognition} via {Large-Scale} {Weak} {Supervision}},
++  author = {Alec Radford and Jong Wook Kim and Tao Xu and Greg Brockman and Christine McLeavey and Ilya Sutskever},
++  year = {2022},
++  eprint = {2212.04356},
++  archiveprefix = {arXiv},
++  howpublished = {arXiv preprint arXiv:2212.04356},
++  note = {verified: arxiv API 2026-11-03}
++}
++
++@misc{brohan2023rt2,
++  title = {{RT-2:} {Vision-Language-Action} {Models} {Transfer} {Web} {Knowledge} to {Robotic} {Control}},
++  author = {Anthony Brohan and Noah Brown and Justice Carbajal and Yevgen Chebotar and Xi Chen and Krzysztof Choromanski and Tianli Ding and Danny Driess and Avinava Dubey and Chelsea Finn and others},
++  year = {2023},
++  eprint = {2307.15818},
++  archiveprefix = {arXiv},
++  howpublished = {arXiv preprint arXiv:2307.15818},
++  note = {verified: arxiv API 2026-11-03}
++}
++
++@misc{liang2024monitoring,
++  title = {Monitoring {AI-Modified} {Content} at {Scale:} {A} {Case} {Study} on the {Impact} of {ChatGPT} on {AI} {Conference} {Peer} {Reviews}},
++  author = {Weixin Liang and Zachary Izzo and Yaohui Zhang and Haley Lepp and Hancheng Cao and Xuandong Zhao and Lingjiao Chen and Haotian Ye and Sheng Liu and Zhi Huang and Daniel A. McFarland and James Y. Zou},
++  year = {2024},
++  eprint = {2403.07183},
++  archiveprefix = {arXiv},
++  howpublished = {arXiv preprint arXiv:2403.07183},
++  note = {verified: arxiv API 2026-11-03}
++}
++
++@misc{liang2024mapping,
++  title = {Mapping the {Increasing} {Use} of {LLMs} in {Scientific} {Papers}},
++  author = {Weixin Liang and Yaohui Zhang and Zhengxuan Wu and Haley Lepp and Wenlong Ji and Xuandong Zhao and Hancheng Cao and Sheng Liu and Siyu He and Zhi Huang and others},
++  year = {2024},
++  eprint = {2404.01268},
++  archiveprefix = {arXiv},
++  howpublished = {arXiv preprint arXiv:2404.01268},
++  note = {verified: arxiv API 2026-11-03}
++}
++
++@misc{geng2024writing,
++  title = {Is {ChatGPT} {Transforming} {Academics'} {Writing} {Style?}},
++  author = {Mingmeng Geng and Roberto Trotta},
++  year = {2024},
++  eprint = {2404.08627},
++  archiveprefix = {arXiv},
++  howpublished = {arXiv preprint arXiv:2404.08627},
++  note = {verified: arxiv API 2026-11-03}
++}
++
++@misc{gray2024contamination,
++  title = {{ChatGPT} "contamination": estimating the prevalence of {LLMs} in the scholarly literature},
++  author = {Andrew Gray},
++  year = {2024},
++  eprint = {2403.16887},
++  archiveprefix = {arXiv},
++  howpublished = {arXiv preprint arXiv:2403.16887},
++  note = {verified: arxiv API 2026-11-03}
++}
++
++@misc{ott2022benchmarks,
++  title = {Mapping global dynamics of benchmark creation and saturation in artificial intelligence},
++  author = {Simon Ott and Adriano Barbosa-Silva and Kathrin Blagec and Jan Brauner and Matthias Samwald},
++  year = {2022},
++  eprint = {2203.04592},
++  archiveprefix = {arXiv},
++  howpublished = {arXiv preprint arXiv:2203.04592},
++  note = {verified: arxiv API 2026-11-03}
++}
++
++@misc{dehghani2021lottery,
++  title = {The {Benchmark} {Lottery}},
++  author = {Mostafa Dehghani and Yi Tay and Alexey A. Gritsenko and Zhe Zhao and Neil Houlsby and Fernando Diaz and Donald Metzler and Oriol Vinyals},
++  year = {2021},
++  eprint = {2107.07002},
++  archiveprefix = {arXiv},
++  howpublished = {arXiv preprint arXiv:2107.07002},
++  note = {verified: arxiv API 2026-11-03}
++}
++
++@misc{raji2021benchmark,
++  title = {{AI} and the {Everything} in the {Whole} {Wide} {World} {Benchmark}},
++  author = {Inioluwa Deborah Raji and Emily M. Bender and Amandalynne Paullada and Emily Denton and Alex Hanna},
++  year = {2021},
++  eprint = {2111.15366},
++  archiveprefix = {arXiv},
++  howpublished = {arXiv preprint arXiv:2111.15366},
++  note = {verified: arxiv API 2026-11-03}
++}
++
++@misc{maslej2024aiindex,
++  title = {Artificial {Intelligence} {Index} {Report} 2024},
++  author = {Nestor Maslej and Loredana Fattorini and Raymond Perrault and Vanessa Parli and Anka Reuel and Erik Brynjolfsson and John Etchemendy and Katrina Ligett and Terah Lyons and James Manyika and others},
++  year = {2024},
++  eprint = {2405.19522},
++  archiveprefix = {arXiv},
++  howpublished = {arXiv preprint arXiv:2405.19522},
++  note = {verified: arxiv API 2026-11-03}
++}
++
++@misc{maslej2025aiindex,
++  title = {Artificial {Intelligence} {Index} {Report} 2025},
++  author = {Nestor Maslej and Loredana Fattorini and Raymond Perrault and Yolanda Gil and Vanessa Parli and Njenga Kariuki and Emily Capstick and Anka Reuel and Erik Brynjolfsson and John Etchemendy and others},
++  year = {2025},
++  eprint = {2504.07139},
++  archiveprefix = {arXiv},
++  howpublished = {arXiv preprint arXiv:2504.07139},
++  note = {verified: arxiv API 2026-11-03}
++}
++
++@misc{pineau2021reproducibility,
++  title = {Improving {Reproducibility} in {Machine} {Learning} {Research} {(A} {Report} from the {NeurIPS} 2019 {Reproducibility} {Program)}},
++  author = {Joelle Pineau and Philippe Vincent-Lamarre and Koustuv Sinha and Vincent Larivière and Alina Beygelzimer and Florence d'Alché-Buc and Emily Fox and Hugo Larochelle},
++  year = {2020},
++  eprint = {2003.12206},
++  archiveprefix = {arXiv},
++  howpublished = {arXiv preprint arXiv:2003.12206},
++  note = {verified: arxiv API 2026-11-03}
++}
++
++@misc{hendrycks2021unsolved,
++  title = {Unsolved {Problems} in {ML} {Safety}},
++  author = {Dan Hendrycks and Nicholas Carlini and John Schulman and Jacob Steinhardt},
++  year = {2021},
++  eprint = {2109.13916},
++  archiveprefix = {arXiv},
++  howpublished = {arXiv preprint arXiv:2109.13916},
++  note = {verified: arxiv API 2026-11-03}
++}
++
++@incollection{ley2002dblp,
++  title = {The {DBLP} {Computer} {Science} {Bibliography:} {Evolution,} {Research} {Issues,} {Perspectives}},
++  author = {Ley, Michael},
++  year = {2002},
++  booktitle = {String Processing and Information Retrieval (SPIRE 2002)},
++  series = {Lecture Notes in Computer Science},
++  pages = {1--10},
++  publisher = {Springer Berlin Heidelberg},
++  doi = {10.1007/3-540-45735-6_1},
++  note = {verified: crossref 2026-11-03}
++}
++
++@article{ginsparg2011arxiv,
++  title = {{ArXiv} at 20},
++  author = {Ginsparg, Paul},
++  year = {2011},
++  journal = {Nature},
++  volume = {476},
++  number = {7359},
++  pages = {145--147},
++  doi = {10.1038/476145a},
++  note = {verified: crossref 2026-11-03}
++}
++
++@article{wang2020mag,
++  title = {Microsoft {Academic} {Graph:} {When} experts are not enough},
++  author = {Wang, Kuansan and Shen, Zhihong and Huang, Chiyuan and Wu, Chieh-Han and Dong, Yuxiao and Kanakia, Anshul},
++  year = {2020},
++  journal = {Quantitative Science Studies},
++  volume = {1},
++  number = {1},
++  pages = {396--413},
++  doi = {10.1162/qss_a_00021},
++  note = {verified: crossref 2026-11-03}
++}
++
++@article{waltman2012classification,
++  title = {{A} new methodology for constructing a publication‐level classification system of science},
++  author = {Waltman, Ludo and van Eck, Nees Jan},
++  year = {2012},
++  journal = {Journal of the American Society for Information Science and Technology},
++  volume = {63},
++  number = {12},
++  pages = {2378--2392},
++  doi = {10.1002/asi.22748},
++  note = {verified: crossref 2026-11-03}
++}
++
++@article{glanzel2003classification,
++  title = {{A} new classification scheme of science fields and subfields designed for scientometric evaluation purposes},
++  author = {Glänzel, Wolfgang and Schubert, András},
++  year = {2003},
++  journal = {Scientometrics},
++  volume = {56},
++  number = {3},
++  pages = {357--367},
++  doi = {10.1023/A:1022378804087},
++  note = {verified: crossref 2026-11-03}
++}
++
++@article{klavans2017taxonomy,
++  title = {Which {Type} of {Citation} {Analysis} {Generates} the {Most} {Accurate} {Taxonomy} of {Scientific} and {Technical} {Knowledge?}},
++  author = {Klavans, Richard and Boyack, Kevin W.},
++  year = {2017},
++  journal = {Journal of the Association for Information Science and Technology},
++  volume = {68},
++  number = {4},
++  pages = {984--998},
++  doi = {10.1002/asi.23734},
++  note = {verified: crossref 2026-11-03}
++}
++
++@article{sjogarde2018granularity,
++  title = {Granularity of algorithmically constructed publication-level classifications of research publications: {Identification} of topics},
++  author = {Sjögårde, Peter and Ahlgren, Per},
++  year = {2018},
++  journal = {Journal of Informetrics},
++  volume = {12},
++  number = {1},
++  pages = {133--152},
++  doi = {10.1016/j.joi.2017.12.006},
++  note = {verified: crossref 2026-11-03}
++}
++
++@article{traag2019leiden,
++  title = {From {Louvain} to {Leiden:} guaranteeing well-connected communities},
++  author = {Traag, V. A. and Waltman, L. and van Eck, N. J.},
++  year = {2019},
++  journal = {Scientific Reports},
++  volume = {9},
++  number = {1},
++  doi = {10.1038/s41598-019-41695-z},
++  note = {verified: crossref 2026-11-03}
++}
++
++@article{rous2012ccs,
++  title = {Major update to {ACM's} {Computing} {Classification} {System}},
++  author = {Rous, Bernard},
++  year = {2012},
++  journal = {Communications of the ACM},
++  volume = {55},
++  number = {11},
++  pages = {12--12},
++  doi = {10.1145/2366316.2366320},
++  note = {verified: crossref 2026-11-03}
++}
++
++@article{vardi2009conferences,
++  title = {Conferences vs. journals in computing research},
++  author = {Vardi, Moshe Y.},
++  year = {2009},
++  journal = {Communications of the ACM},
++  volume = {52},
++  number = {5},
++  pages = {5--5},
++  doi = {10.1145/1506409.1506410},
++  note = {verified: crossref 2026-11-03}
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++  journal = {Science Advances},
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++  title = {Opinion {Paper:} {“So} what if {ChatGPT} wrote it?” {Multidisciplinary} perspectives on opportunities, challenges and implications of generative conversational {AI} for research, practice and policy},
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++  journal = {International Journal of Information Management},
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++  note = {verified: crossref 2026-11-03}
++}
++
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++  title = {{AI} and science: what 1,600 researchers think},
++  author = {Van Noorden, Richard and Perkel, Jeffrey M.},
++  year = {2023},
++  journal = {Nature},
++  volume = {621},
++  number = {7980},
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++  note = {verified: crossref 2026-11-03}
++}
++
++@article{messeri2024illusions,
++  title = {Artificial intelligence and illusions of understanding in scientific research},
++  author = {Messeri, Lisa and Crockett, M. J.},
++  year = {2024},
++  journal = {Nature},
++  volume = {627},
++  number = {8002},
++  pages = {49--58},
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++}
++
++@article{birhane2023science,
++  title = {Science in the age of large language models},
++  author = {Birhane, Abeba and Kasirzadeh, Atoosa and Leslie, David and Wachter, Sandra},
++  year = {2023},
++  journal = {Nature Reviews Physics},
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++}
++
++@article{krenn2023forecasting,
++  title = {Forecasting the future of artificial intelligence with machine learning-based link prediction in an exponentially growing knowledge network},
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++  year = {2023},
++  journal = {Nature Machine Intelligence},
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++}
++
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++  title = {Science of science},
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++  year = {2018},
++  journal = {Science},
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++  number = {6379},
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++  note = {verified: crossref 2026-11-03}
++}
++
++@book{wang2021sos,
++  title = {The {Science} of {Science}},
++  author = {Wang, Dashun and Barabási, Albert-László},
++  year = {2021},
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++  note = {verified: crossref 2026-11-03}
++}
++
++@article{park2023disruptive,
++  title = {Papers and patents are becoming less disruptive over time},
++  author = {Park, Michael and Leahey, Erin and Funk, Russell J.},
++  year = {2023},
++  journal = {Nature},
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++  number = {7942},
++  pages = {138--144},
++  doi = {10.1038/s41586-022-05543-x},
++  note = {verified: crossref 2026-11-03}
++}
++
++@article{chu2021slowed,
++  title = {Slowed canonical progress in large fields of science},
++  author = {Chu, Johan S. G. and Evans, James A.},
++  year = {2021},
++  journal = {Proceedings of the National Academy of Sciences},
++  volume = {118},
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++  doi = {10.1073/pnas.2021636118},
++  note = {verified: crossref 2026-11-03}
++}
++
++@article{bornmann2015growth,
++  title = {Growth rates of modern science: {A} bibliometric analysis based on the number of publications and cited references},
++  author = {Bornmann, Lutz and Mutz, Rüdiger},
++  year = {2015},
++  journal = {Journal of the Association for Information Science and Technology},
++  volume = {66},
++  number = {11},
++  pages = {2215--2222},
++  doi = {10.1002/asi.23329},
++  note = {verified: crossref 2026-11-03}
++}
++
++@article{binnashwan2023chatgpt,
++  title = {Use of {ChatGPT} in academia: {Academic} integrity hangs in the balance},
++  author = {Bin-Nashwan, Saeed Awadh and Sadallah, Mouad and Bouteraa, Mohamed},
++  year = {2023},
++  journal = {Technology in Society},
++  volume = {75},
++  pages = {102370},
++  doi = {10.1016/j.techsoc.2023.102370},
++  note = {verified: crossref 2026-11-03}
++}
++
++@article{kapoor2023leakage,
++  title = {Leakage and the reproducibility crisis in machine-learning-based science},
++  author = {Kapoor, Sayash and Narayanan, Arvind},
++  year = {2023},
++  journal = {Patterns},
++  volume = {4},
++  number = {9},
++  pages = {100804},
++  doi = {10.1016/j.patter.2023.100804},
++  note = {verified: crossref 2026-11-03}
++}
++
++@article{gundersen2018reproducibility,
++  title = {State of the {Art:} {Reproducibility} in {Artificial} {Intelligence}},
++  author = {Gundersen, Odd Erik and Kjensmo, Sigbjørn},
++  year = {2018},
++  journal = {Proceedings of the AAAI Conference on Artificial Intelligence},
++  volume = {32},
++  number = {1},
++  doi = {10.1609/aaai.v32i1.11503},
++  note = {verified: crossref 2026-11-03}
++}
++
++@misc{liang2025widespread,
++  title = {The {Widespread} {Adoption} of {Large} {Language} {Model-Assisted} {Writing} {Across} {Society}},
++  author = {Weixin Liang and Yaohui Zhang and Mihai Codreanu and Jiayu Wang and Hancheng Cao and James Zou},
++  year = {2025},
++  eprint = {2502.09747},
++  archiveprefix = {arXiv},
++  howpublished = {arXiv preprint arXiv:2502.09747},
++  note = {verified: arxiv API 2026-11-03}
++}
++
++@misc{openai2022chatgpt,
++  title = {Introducing {ChatGPT}},
++  author = {{OpenAI}},
++  year = {2022},
++  howpublished = {Blog post, 30 November 2022, \url{https://openai.com/index/chatgpt/}},
++  note = {Accessed 3 November 2026; original URL openai.com/blog/chatgpt archived by the Internet Archive on 2 December 2022}
++}
++@misc{openalex2025topics,
++  title = {Topics},
++  author = {{OpenAlex}},
++  year = {2025},
++  howpublished = {OpenAlex documentation, \url{https://help.openalex.org/data/topics/}},
++  note = {Accessed 3 November 2026}
++}
++@misc{arxiv2026stats,
++  title = {{arXiv} monthly submissions},
++  author = {{arXiv}},
++  year = {2026},
++  howpublished = {\url{https://arxiv.org/stats/monthly_submissions}},
++  note = {Accessed 3 November 2026}
++}

modified manuscript/references_part03.bib +4 −157

				`@@ -1,15 +1,9 @@`
1	% references_part03.bib — Part III (Data and Systematic Review Protocol). Every entry verified by a single	1	% references_part03.bib — Part III (Data and Systematic Review Protocol). Every entry verified by a single
2	% Crossref or arXiv API lookup on 2026-11-03; see references_verification_part03.csv.	2	% Crossref or arXiv API lookup on 2026-11-03; see references_verification_part03.csv.
3	−@article{page2021prisma,	3	+% Keys verified here but already defined (same DOI) in references.bib by Agent B-lit are NOT redefined:
4	− author = {Page, Matthew J. and McKenzie, Joanne E. and Bossuyt, Patrick M. and Boutron, Isabelle and Hoffmann, Tammy C. and Mulrow, Cynthia D. and Shamseer, Larissa and Tetzlaff, Jennifer M. and Akl, Elie A. and Brennan, Sue E. and Chou, Roger and Glanville, Julie and Grimshaw, Jeremy M. and Hr{\'o}bjartsson, Asbj{\o}rn and Lalu, Manoj M. and Li, Tianjing and Loder, Elizabeth W. and Mayo-Wilson, Evan and McDonald, Steve and McGuinness, Luke A. and Stewart, Lesley A. and Thomas, James and Tricco, Andrea C. and Welch, Vivian A. and Whiting, Penny and Moher, David},	4	+% alperin2024openalex, ginsparg2011arxiv, haddaway2022prismaflow, hendricks2020crossref, ley2002dblp, ley2009dblp, page2021prisma, petersen2008mapping, petersen2015mapping, priem2022openalex, rethlefsen2021prismas, tricco2018prismascr, visser2021comparison, waltman2016review, wang2020mag
5	− title = {The {PRISMA} 2020 statement: an updated guideline for reporting systematic reviews},	5	+% (their verification rows remain in references_verification_part03.csv with note=reused:references.bib).
6	− journal = {BMJ},	6	+
7	− year = {2021},
8	− volume = {372},
9	− pages = {n71},
10	− doi = {10.1136/bmj.n71},
11	− note = {verified: crossref 2026-11-03}
12	−}
13	@article{page2021prismaee,	7	@article{page2021prismaee,
14	author = {Page, Matthew J. and Moher, David and Bossuyt, Patrick M. and Boutron, Isabelle and Hoffmann, Tammy C. and Mulrow, Cynthia D. and Shamseer, Larissa and Tetzlaff, Jennifer M. and Akl, Elie A. and Brennan, Sue E. and Chou, Roger and Glanville, Julie and Grimshaw, Jeremy M. and Hr{\'o}bjartsson, Asbj{\o}rn and Lalu, Manoj M. and Li, Tianjing and Loder, Elizabeth W. and Mayo-Wilson, Evan and McDonald, Steve and McGuinness, Luke A. and Stewart, Lesley A. and Thomas, James and Tricco, Andrea C. and Welch, Vivian A. and Whiting, Penny and McKenzie, Joanne E.},	8	author = {Page, Matthew J. and Moher, David and Bossuyt, Patrick M. and Boutron, Isabelle and Hoffmann, Tammy C. and Mulrow, Cynthia D. and Shamseer, Larissa and Tetzlaff, Jennifer M. and Akl, Elie A. and Brennan, Sue E. and Chou, Roger and Glanville, Julie and Grimshaw, Jeremy M. and Hr{\'o}bjartsson, Asbj{\o}rn and Lalu, Manoj M. and Li, Tianjing and Loder, Elizabeth W. and Mayo-Wilson, Evan and McDonald, Steve and McGuinness, Luke A. and Stewart, Lesley A. and Thomas, James and Tricco, Andrea C. and Welch, Vivian A. and Whiting, Penny and McKenzie, Joanne E.},
15	title = {{PRISMA} 2020 explanation and elaboration: updated guidance and exemplars for reporting systematic reviews},	9	title = {{PRISMA} 2020 explanation and elaboration: updated guidance and exemplars for reporting systematic reviews},
				`@@ -20,58 +14,6 @@`
20	doi = {10.1136/bmj.n160},	14	doi = {10.1136/bmj.n160},
21	note = {verified: crossref 2026-11-03}	15	note = {verified: crossref 2026-11-03}
22	}	16	}
23	−@inproceedings{petersen2008mapping,
24	− author = {Petersen, Kai and Feldt, Robert and Mujtaba, Shahid and Mattsson, Michael},
25	− title = {Systematic mapping studies in software engineering},
26	− booktitle = {Proceedings of the 12th International Conference on Evaluation and Assessment in Software Engineering (EASE)},
27	− series = {Electronic Workshops in Computing},
28	− publisher = {BCS Learning and Development},
29	− year = {2008},
30	− doi = {10.14236/ewic/EASE2008.8},
31	− note = {verified: crossref 2026-11-03}
32	−}
33	−@article{petersen2015mapping,
34	− author = {Petersen, Kai and Vakkalanka, Sairam and Kuzniarz, Ludwik},
35	− title = {Guidelines for conducting systematic mapping studies in software engineering: An update},
36	− journal = {Information and Software Technology},
37	− year = {2015},
38	− volume = {64},
39	− pages = {1--18},
40	− doi = {10.1016/j.infsof.2015.03.007},
41	− note = {verified: crossref 2026-11-03}
42	−}
43	−@article{tricco2018prismascr,
44	− author = {Tricco, Andrea C. and Lillie, Erin and Zarin, Wasifa and O'Brien, Kelly K. and Colquhoun, Heather and Levac, Danielle and Moher, David and Peters, Micah D. J. and Horsley, Tanya and Weeks, Laura and Hempel, Susanne and Akl, Elie A. and Chang, Christine and McGowan, Jessie and Stewart, Lesley and Hartling, Lisa and Aldcroft, Adrian and Wilson, Michael G. and Garritty, Chantelle and Lewin, Simon and Godfrey, Christina M. and Macdonald, Marilyn T. and Langlois, Etienne V. and Soares-Weiser, Karla and Moriarty, Jo and Clifford, Tammy and Tun{\c{c}}alp, {\"O}zge and Straus, Sharon E.},
45	− title = {{PRISMA} extension for scoping reviews ({PRISMA-ScR}): Checklist and explanation},
46	− journal = {Annals of Internal Medicine},
47	− year = {2018},
48	− volume = {169},
49	− number = {7},
50	− pages = {467--473},
51	− doi = {10.7326/M18-0850},
52	− note = {verified: crossref 2026-11-03}
53	−}
54	−@article{rethlefsen2021prismas,
55	− author = {Rethlefsen, Melissa L. and Kirtley, Shona and Waffenschmidt, Siw and Ayala, Ana Patricia and Moher, David and Page, Matthew J. and Koffel, Jonathan B.},
56	− title = {{PRISMA-S}: an extension to the {PRISMA} Statement for Reporting Literature Searches in Systematic Reviews},
57	− journal = {Systematic Reviews},
58	− year = {2021},
59	− volume = {10},
60	− pages = {39},
61	− doi = {10.1186/s13643-020-01542-z},
62	− note = {verified: crossref 2026-11-03}
63	−}
64	−@article{haddaway2022prismaflow,
65	− author = {Haddaway, Neal R. and Page, Matthew J. and Pritchard, Chris C. and McGuinness, Luke A.},
66	− title = {{PRISMA2020}: An {R} package and {Shiny} app for producing {PRISMA} 2020-compliant flow diagrams, with interactivity for optimised digital transparency and Open Synthesis},
67	− journal = {Campbell Systematic Reviews},
68	− year = {2022},
69	− volume = {18},
70	− number = {2},
71	− pages = {e1230},
72	− doi = {10.1002/cl2.1230},
73	− note = {verified: crossref 2026-11-03}
74	−}
75	@article{gusenbauer2020search,	17	@article{gusenbauer2020search,
76	author = {Gusenbauer, Michael and Haddaway, Neal R.},	18	author = {Gusenbauer, Michael and Haddaway, Neal R.},
77	title = {Which academic search systems are suitable for systematic reviews or meta-analyses? {Evaluating} retrieval qualities of {Google Scholar}, {PubMed}, and 26 other resources},	19	title = {Which academic search systems are suitable for systematic reviews or meta-analyses? {Evaluating} retrieval qualities of {Google Scholar}, {PubMed}, and 26 other resources},
				`@@ -83,39 +25,6 @@`
83	doi = {10.1002/jrsm.1378},	25	doi = {10.1002/jrsm.1378},
84	note = {verified: crossref 2026-11-03}	26	note = {verified: crossref 2026-11-03}
85	}	27	}
86	−@incollection{ley2002dblp,
87	− author = {Ley, Michael},
88	− title = {The {DBLP} computer science bibliography: Evolution, research issues, perspectives},
89	− booktitle = {String Processing and Information Retrieval (SPIRE 2002)},
90	− series = {Lecture Notes in Computer Science},
91	− volume = {2476},
92	− publisher = {Springer},
93	− year = {2002},
94	− pages = {1--10},
95	− doi = {10.1007/3-540-45735-6_1},
96	− note = {verified: crossref 2026-11-03}
97	−}
98	−@article{ley2009dblp,
99	− author = {Ley, Michael},
100	− title = {{DBLP}: some lessons learned},
101	− journal = {Proceedings of the VLDB Endowment},
102	− year = {2009},
103	− volume = {2},
104	− number = {2},
105	− pages = {1493--1500},
106	− doi = {10.14778/1687553.1687577},
107	− note = {verified: crossref 2026-11-03}
108	−}
109	−@article{ginsparg2011arxiv,
110	− author = {Ginsparg, Paul},
111	− title = {{ArXiv} at 20},
112	− journal = {Nature},
113	− year = {2011},
114	− volume = {476},
115	− pages = {145--147},
116	− doi = {10.1038/476145a},
117	− note = {verified: crossref 2026-11-03}
118	−}
119	@misc{clement2019arxiv,	28	@misc{clement2019arxiv,
120	author = {Clement, Colin B. and Bierbaum, Matthew and O'Keeffe, Kevin P. and Alemi, Alexander A.},	29	author = {Clement, Colin B. and Bierbaum, Matthew and O'Keeffe, Kevin P. and Alemi, Alexander A.},
121	title = {On the use of {arXiv} as a dataset},	30	title = {On the use of {arXiv} as a dataset},
				`@@ -125,15 +34,6 @@`
125	howpublished = {arXiv preprint arXiv:1905.00075},	34	howpublished = {arXiv preprint arXiv:1905.00075},
126	note = {verified: arxiv 2026-11-03}	35	note = {verified: arxiv 2026-11-03}
127	}	36	}
128	−@misc{priem2022openalex,
129	− author = {Priem, Jason and Piwowar, Heather and Orr, Richard},
130	− title = {{OpenAlex}: A fully-open index of scholarly works, authors, venues, institutions, and concepts},
131	− year = {2022},
132	− eprint = {2205.01833},
133	− archiveprefix = {arXiv},
134	− howpublished = {arXiv preprint arXiv:2205.01833},
135	− note = {verified: arxiv 2026-11-03}
136	−}
137	@misc{kinney2023s2,	37	@misc{kinney2023s2,
138	author = {Kinney, Rodney and Anastasiades, Chloe and Authur, Russell and Beltagy, Iz and Bragg, Jonathan and Buraczynski, Alexandra and Cachola, Isabel and Candra, Stefan and Chandrasekhar, Yoganand and Cohan, Arman and others},	38	author = {Kinney, Rodney and Anastasiades, Chloe and Authur, Russell and Beltagy, Iz and Bragg, Jonathan and Buraczynski, Alexandra and Cachola, Isabel and Candra, Stefan and Chandrasekhar, Yoganand and Cohan, Arman and others},
139	title = {The {Semantic Scholar} Open Data Platform},	39	title = {The {Semantic Scholar} Open Data Platform},
				`@@ -143,15 +43,6 @@`
143	howpublished = {arXiv preprint arXiv:2301.10140},	43	howpublished = {arXiv preprint arXiv:2301.10140},
144	note = {verified: arxiv 2026-11-03}	44	note = {verified: arxiv 2026-11-03}
145	}	45	}
146	−@misc{alperin2024openalex,
147	− author = {Alperin, Juan Pablo and Portenoy, Jason and Demes, Kyle and Larivi{\`e}re, Vincent and Haustein, Stefanie},
148	− title = {An analysis of the suitability of {OpenAlex} for bibliometric analyses},
149	− year = {2024},
150	− eprint = {2404.17663},
151	− archiveprefix = {arXiv},
152	− howpublished = {arXiv preprint arXiv:2404.17663},
153	− note = {verified: arxiv 2026-11-03}
154	−}
155	@article{culbert2025openalex,	46	@article{culbert2025openalex,
156	author = {Culbert, Jack H. and Hobert, Anne and Jahn, Najko and Haupka, Nick and Schmidt, Marion and Donner, Paul and Mayr, Philipp},	47	author = {Culbert, Jack H. and Hobert, Anne and Jahn, Najko and Haupka, Nick and Schmidt, Marion and Donner, Paul and Mayr, Philipp},
157	title = {Reference coverage analysis of {OpenAlex} compared to {Web of Science} and {Scopus}},	48	title = {Reference coverage analysis of {OpenAlex} compared to {Web of Science} and {Scopus}},
				`@@ -181,17 +72,6 @@`
181	doi = {10.1162/qss_a_00286},	72	doi = {10.1162/qss_a_00286},
182	note = {verified: crossref 2026-11-03}	73	note = {verified: crossref 2026-11-03}
183	}	74	}
184	−@article{visser2021comparison,
185	− author = {Visser, Martijn and van Eck, Nees Jan and Waltman, Ludo},
186	− title = {Large-scale comparison of bibliographic data sources: {Scopus}, {Web of Science}, {Dimensions}, {Crossref}, and {Microsoft Academic}},
187	− journal = {Quantitative Science Studies},
188	− year = {2021},
189	− volume = {2},
190	− number = {1},
191	− pages = {20--41},
192	− doi = {10.1162/qss_a_00112},
193	− note = {verified: crossref 2026-11-03}
194	−}
195	@article{martinmartin2021comparison,	75	@article{martinmartin2021comparison,
196	author = {Mart{\'i}n-Mart{\'i}n, Alberto and Thelwall, Mike and Orduna-Malea, Enrique and Delgado L{\'o}pez-C{\'o}zar, Emilio},	76	author = {Mart{\'i}n-Mart{\'i}n, Alberto and Thelwall, Mike and Orduna-Malea, Enrique and Delgado L{\'o}pez-C{\'o}zar, Emilio},
197	title = {{Google Scholar}, {Microsoft Academic}, {Scopus}, {Dimensions}, {Web of Science}, and {OpenCitations}' {COCI}: a multidisciplinary comparison of coverage via citations},	77	title = {{Google Scholar}, {Microsoft Academic}, {Scopus}, {Dimensions}, {Web of Science}, and {OpenCitations}' {COCI}: a multidisciplinary comparison of coverage via citations},
				`@@ -212,28 +92,6 @@`
212	doi = {10.1007/s11192-015-1765-5},	92	doi = {10.1007/s11192-015-1765-5},
213	note = {verified: crossref 2026-11-03}	93	note = {verified: crossref 2026-11-03}
214	}	94	}
215	−@article{hendricks2020crossref,
216	− author = {Hendricks, Ginny and Tkaczyk, Dominika and Lin, Jennifer and Feeney, Patricia},
217	− title = {{Crossref}: The sustainable source of community-owned scholarly metadata},
218	− journal = {Quantitative Science Studies},
219	− year = {2020},
220	− volume = {1},
221	− number = {1},
222	− pages = {414--427},
223	− doi = {10.1162/qss_a_00022},
224	− note = {verified: crossref 2026-11-03}
225	−}
226	−@article{wang2020mag,
227	− author = {Wang, Kuansan and Shen, Zhihong and Huang, Chiyuan and Wu, Chieh-Han and Dong, Yuxiao and Kanakia, Anshul},
228	− title = {{Microsoft Academic Graph}: When experts are not enough},
229	− journal = {Quantitative Science Studies},
230	− year = {2020},
231	− volume = {1},
232	− number = {1},
233	− pages = {396--413},
234	− doi = {10.1162/qss_a_00021},
235	− note = {verified: crossref 2026-11-03}
236	−}
237	@article{radev2013acl,	95	@article{radev2013acl,
238	author = {Radev, Dragomir R. and Muthukrishnan, Pradeep and Qazvinian, Vahed and Abu-Jbara, Amjad},	96	author = {Radev, Dragomir R. and Muthukrishnan, Pradeep and Qazvinian, Vahed and Abu-Jbara, Amjad},
239	title = {The {ACL} anthology network corpus},	97	title = {The {ACL} anthology network corpus},
				`@@ -294,17 +152,6 @@`
294	doi = {10.1007/s11192-012-0775-9},	152	doi = {10.1007/s11192-012-0775-9},
295	note = {verified: crossref 2026-11-03 (online 2012, volume 94 = 2013)}	153	note = {verified: crossref 2026-11-03 (online 2012, volume 94 = 2013)}
296	}	154	}
297	−@article{waltman2016review,
298	− author = {Waltman, Ludo},
299	− title = {A review of the literature on citation impact indicators},
300	− journal = {Journal of Informetrics},
301	− year = {2016},
302	− volume = {10},
303	− number = {2},
304	− pages = {365--391},
305	− doi = {10.1016/j.joi.2016.02.007},
306	− note = {verified: crossref 2026-11-03}
307	−}
308	@article{fortunato2018science,	155	@article{fortunato2018science,
309	author = {Fortunato, Santo and Bergstrom, Carl T. and B{\"o}rner, Katy and Evans, James A. and Helbing, Dirk and Milojevi{\'c}, Sta{\v{s}}a and Petersen, Alexander M. and Radicchi, Filippo and Sinatra, Roberta and Uzzi, Brian and Vespignani, Alessandro and Waltman, Ludo and Wang, Dashun and Barab{\'a}si, Albert-L{\'a}szl{\'o}},	156	author = {Fortunato, Santo and Bergstrom, Carl T. and B{\"o}rner, Katy and Evans, James A. and Helbing, Dirk and Milojevi{\'c}, Sta{\v{s}}a and Petersen, Alexander M. and Radicchi, Filippo and Sinatra, Roberta and Uzzi, Brian and Vespignani, Alessandro and Waltman, Ludo and Wang, Dashun and Barab{\'a}si, Albert-L{\'a}szl{\'o}},
310	title = {Science of science},	157	title = {Science of science},

added manuscript/references_part04.bib +492 −0

++% references_part04.bib — Part IV (Computational Methodology), WP44.
++% Every entry verified on 2026-11-03 by a Crossref / OpenAlex / arXiv lookup; see references_verification_part04.csv.
++
++@article{waltman2016review,
++  author  = {Waltman, Ludo},
++  title   = {A review of the literature on citation impact indicators},
++  journal = {Journal of Informetrics},
++  year    = {2016},
++  volume  = {10},
++  number  = {2},
++  pages   = {365--391},
++  doi     = {10.1016/j.joi.2016.02.007}
++}
++
++@incollection{waltman2019fieldnorm,
++  author    = {Waltman, Ludo and van Eck, Nees Jan},
++  title     = {Field Normalization of Scientometric Indicators},
++  booktitle = {Springer Handbook of Science and Technology Indicators},
++  publisher = {Springer},
++  year      = {2019},
++  pages     = {281--300},
++  doi       = {10.1007/978-3-030-02511-3_11}
++}
++
++@article{wang2013window,
++  author  = {Wang, Jian},
++  title   = {Citation time window choice for research impact evaluation},
++  journal = {Scientometrics},
++  year    = {2013},
++  volume  = {94},
++  number  = {3},
++  pages   = {851--872},
++  doi     = {10.1007/s11192-012-0775-9}
++}
++
++@misc{openalex2026fwci,
++  author       = {{OpenAlex}},
++  title        = {Works: attributes ({F}ield-{W}eighted {C}itation {I}mpact, \texttt{fwci})},
++  howpublished = {OpenAlex documentation, \url{https://help.openalex.org/data/works/attributes/}},
++  year         = {2026},
++  note         = {Accessed 3 November 2026}
++}
++
++@article{ceriani2012gini,
++  author  = {Ceriani, Lidia and Verme, Paolo},
++  title   = {The origins of the {G}ini index: extracts from {V}ariabilit{\`a} e {M}utabilit{\`a} (1912) by {C}orrado {G}ini},
++  journal = {The Journal of Economic Inequality},
++  year    = {2012},
++  volume  = {10},
++  number  = {3},
++  pages   = {421--443},
++  doi     = {10.1007/s10888-011-9188-x}
++}
++
++@article{hirschman1964paternity,
++  author  = {Hirschman, Albert O.},
++  title   = {The paternity of an index},
++  journal = {American Economic Review},
++  year    = {1964},
++  volume  = {54},
++  number  = {5},
++  pages   = {761--762},
++  note    = {OpenAlex W2163989766}
++}
++
++@article{rao1982diversity,
++  author  = {Rao, C. Radhakrishna},
++  title   = {Diversity and dissimilarity coefficients: a unified approach},
++  journal = {Theoretical Population Biology},
++  year    = {1982},
++  volume  = {21},
++  number  = {1},
++  pages   = {24--43},
++  doi     = {10.1016/0040-5809(82)90004-1}
++}
++
++@article{stirling2007diversity,
++  author  = {Stirling, Andy},
++  title   = {A general framework for analysing diversity in science, technology and society},
++  journal = {Journal of The Royal Society Interface},
++  year    = {2007},
++  volume  = {4},
++  number  = {15},
++  pages   = {707--719},
++  doi     = {10.1098/rsif.2007.0213}
++}
++
++@article{leinster2012diversity,
++  author  = {Leinster, Tom and Cobbold, Christina A.},
++  title   = {Measuring diversity: the importance of species similarity},
++  journal = {Ecology},
++  year    = {2012},
++  volume  = {93},
++  number  = {3},
++  pages   = {477--489},
++  doi     = {10.1890/10-2402.1}
++}
++
++@article{rafols2010diversity,
++  author  = {Rafols, Ismael and Meyer, Martin},
++  title   = {Diversity and network coherence as indicators of interdisciplinarity: case studies in bionanoscience},
++  journal = {Scientometrics},
++  year    = {2010},
++  volume  = {82},
++  number  = {2},
++  pages   = {263--287},
++  doi     = {10.1007/s11192-009-0041-y}
++}
++
++@article{zhang2016diversity,
++  author  = {Zhang, Lin and Rousseau, Ronald and Gl{\"a}nzel, Wolfgang},
++  title   = {Diversity of references as an indicator of the interdisciplinarity of journals: taking similarity between subject fields into account},
++  journal = {Journal of the Association for Information Science and Technology},
++  year    = {2016},
++  volume  = {67},
++  number  = {5},
++  pages   = {1257--1265},
++  doi     = {10.1002/asi.23487}
++}
++
++@article{newman2004community,
++  author  = {Newman, M. E. J. and Girvan, M.},
++  title   = {Finding and evaluating community structure in networks},
++  journal = {Physical Review E},
++  year    = {2004},
++  volume  = {69},
++  number  = {2},
++  pages   = {026113},
++  doi     = {10.1103/PhysRevE.69.026113}
++}
++
++@article{leicht2008directed,
++  author  = {Leicht, E. A. and Newman, M. E. J.},
++  title   = {Community structure in directed networks},
++  journal = {Physical Review Letters},
++  year    = {2008},
++  volume  = {100},
++  number  = {11},
++  pages   = {118703},
++  doi     = {10.1103/PhysRevLett.100.118703}
++}
++
++@article{fortunato2007resolution,
++  author  = {Fortunato, Santo and Barth{\'e}lemy, Marc},
++  title   = {Resolution limit in community detection},
++  journal = {Proceedings of the National Academy of Sciences},
++  year    = {2007},
++  volume  = {104},
++  number  = {1},
++  pages   = {36--41},
++  doi     = {10.1073/pnas.0605965104}
++}
++
++@article{fortunato2010community,
++  author  = {Fortunato, Santo},
++  title   = {Community detection in graphs},
++  journal = {Physics Reports},
++  year    = {2010},
++  volume  = {486},
++  number  = {3--5},
++  pages   = {75--174},
++  doi     = {10.1016/j.physrep.2009.11.002}
++}
++
++@inproceedings{cohan2020specter,
++  author    = {Cohan, Arman and Feldman, Sergey and Beltagy, Iz and Downey, Doug and Weld, Daniel S.},
++  title     = {{SPECTER}: Document-level representation learning using citation-informed transformers},
++  booktitle = {Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics},
++  year      = {2020},
++  pages     = {2270--2282},
++  doi       = {10.18653/v1/2020.acl-main.207}
++}
++
++@inproceedings{singh2023scirepeval,
++  author    = {Singh, Amanpreet and D'Arcy, Mike and Cohan, Arman and Downey, Doug and Feldman, Sergey},
++  title     = {{SciRepEval}: A multi-format benchmark for scientific document representations},
++  booktitle = {Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing},
++  year      = {2023},
++  pages     = {5548--5566},
++  doi       = {10.18653/v1/2023.emnlp-main.338}
++}
++
++@inproceedings{ostendorff2022scincl,
++  author    = {Ostendorff, Malte and Rethmeier, Nils and Augenstein, Isabelle and Gipp, Bela and Rehm, Georg},
++  title     = {Neighborhood contrastive learning for scientific document representations with citation embeddings},
++  booktitle = {Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing},
++  year      = {2022},
++  doi       = {10.18653/v1/2022.emnlp-main.802}
++}
++
++@inproceedings{reimers2019sbert,
++  author    = {Reimers, Nils and Gurevych, Iryna},
++  title     = {{Sentence-BERT}: Sentence embeddings using {S}iamese {BERT}-networks},
++  booktitle = {Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing},
++  year      = {2019},
++  pages     = {3980--3990},
++  doi       = {10.18653/v1/D19-1410}
++}
++
++@misc{mcinnes2018umap,
++  author        = {McInnes, Leland and Healy, John and Melville, James},
++  title         = {{UMAP}: Uniform manifold approximation and projection for dimension reduction},
++  year          = {2018},
++  howpublished  = {arXiv preprint arXiv:1802.03426},
++  note          = {Preprint}
++}
++
++@article{mcinnes2017hdbscan,
++  author  = {McInnes, Leland and Healy, John and Astels, Steve},
++  title   = {hdbscan: Hierarchical density based clustering},
++  journal = {The Journal of Open Source Software},
++  year    = {2017},
++  volume  = {2},
++  number  = {11},
++  pages   = {205},
++  doi     = {10.21105/joss.00205}
++}
++
++@inproceedings{campello2013hdbscan,
++  author    = {Campello, Ricardo J. G. B. and Moulavi, Davoud and Sander, Joerg},
++  title     = {Density-based clustering based on hierarchical density estimates},
++  booktitle = {Advances in Knowledge Discovery and Data Mining (PAKDD 2013), Lecture Notes in Computer Science},
++  publisher = {Springer},
++  year      = {2013},
++  pages     = {160--172},
++  doi       = {10.1007/978-3-642-37456-2_14}
++}
++
++@article{hubert1985comparing,
++  author  = {Hubert, Lawrence and Arabie, Phipps},
++  title   = {Comparing partitions},
++  journal = {Journal of Classification},
++  year    = {1985},
++  volume  = {2},
++  number  = {1},
++  pages   = {193--218},
++  doi     = {10.1007/BF01908075}
++}
++
++@article{uzzi2013atypical,
++  author  = {Uzzi, Brian and Mukherjee, Satyam and Stringer, Michael and Jones, Ben},
++  title   = {Atypical combinations and scientific impact},
++  journal = {Science},
++  year    = {2013},
++  volume  = {342},
++  number  = {6157},
++  pages   = {468--472},
++  doi     = {10.1126/science.1240474}
++}
++
++@article{wang2017novelty,
++  author  = {Wang, Jian and Veugelers, Reinhilde and Stephan, Paula},
++  title   = {Bias against novelty in science: A cautionary tale for users of bibliometric indicators},
++  journal = {Research Policy},
++  year    = {2017},
++  volume  = {46},
++  number  = {8},
++  pages   = {1416--1436},
++  doi     = {10.1016/j.respol.2017.06.006}
++}
++
++@article{bernal2017its,
++  author  = {Bernal, James Lopez and Cummins, Steven and Gasparrini, Antonio},
++  title   = {Interrupted time series regression for the evaluation of public health interventions: a tutorial},
++  journal = {International Journal of Epidemiology},
++  year    = {2017},
++  volume  = {46},
++  number  = {1},
++  pages   = {348--355},
++  doi     = {10.1093/ije/dyw098}
++}
++
++@article{bernal2018controls,
++  author  = {Lopez Bernal, James and Cummins, Steven and Gasparrini, Antonio},
++  title   = {The use of controls in interrupted time series studies of public health interventions},
++  journal = {International Journal of Epidemiology},
++  year    = {2018},
++  volume  = {47},
++  number  = {6},
++  pages   = {2082--2093},
++  doi     = {10.1093/ije/dyy135}
++}
++
++@article{newey1987hac,
++  author  = {Newey, Whitney K. and West, Kenneth D.},
++  title   = {A simple, positive semi-definite, heteroskedasticity and autocorrelation consistent covariance matrix},
++  journal = {Econometrica},
++  year    = {1987},
++  volume  = {55},
++  number  = {3},
++  pages   = {703--708},
++  doi     = {10.2307/1913610}
++}
++
++@article{andrews1991hac,
++  author  = {Andrews, Donald W. K.},
++  title   = {Heteroskedasticity and autocorrelation consistent covariance matrix estimation},
++  journal = {Econometrica},
++  year    = {1991},
++  volume  = {59},
++  number  = {3},
++  pages   = {817--858},
++  doi     = {10.2307/2938229}
++}
++
++@article{bai1998breaks,
++  author  = {Bai, Jushan and Perron, Pierre},
++  title   = {Estimating and testing linear models with multiple structural changes},
++  journal = {Econometrica},
++  year    = {1998},
++  volume  = {66},
++  number  = {1},
++  pages   = {47--78},
++  doi     = {10.2307/2998540}
++}
++
++@article{bai2003computation,
++  author  = {Bai, Jushan and Perron, Pierre},
++  title   = {Computation and analysis of multiple structural change models},
++  journal = {Journal of Applied Econometrics},
++  year    = {2003},
++  volume  = {18},
++  number  = {1},
++  pages   = {1--22},
++  doi     = {10.1002/jae.659}
++}
++
++@article{liu1997segmented,
++  author  = {Liu, Jian and Wu, Shiying and Zidek, James V.},
++  title   = {On segmented multivariate regression},
++  journal = {Statistica Sinica},
++  year    = {1997},
++  volume  = {7},
++  number  = {2},
++  pages   = {497--525},
++  note    = {OpenAlex W2142183678}
++}
++
++@article{schwarz1978bic,
++  author  = {Schwarz, Gideon},
++  title   = {Estimating the dimension of a model},
++  journal = {The Annals of Statistics},
++  year    = {1978},
++  volume  = {6},
++  number  = {2},
++  pages   = {461--464},
++  doi     = {10.1214/aos/1176344136}
++}
++
++@article{killick2012pelt,
++  author  = {Killick, R. and Fearnhead, P. and Eckley, I. A.},
++  title   = {Optimal detection of changepoints with a linear computational cost},
++  journal = {Journal of the American Statistical Association},
++  year    = {2012},
++  volume  = {107},
++  number  = {500},
++  pages   = {1590--1598},
++  doi     = {10.1080/01621459.2012.737745}
++}
++
++@article{truong2020ruptures,
++  author  = {Truong, Charles and Oudre, Laurent and Vayatis, Nicolas},
++  title   = {Selective review of offline change point detection methods},
++  journal = {Signal Processing},
++  year    = {2020},
++  volume  = {167},
++  pages   = {107299},
++  doi     = {10.1016/j.sigpro.2019.107299}
++}
++
++@article{yao1988schwarz,
++  author  = {Yao, Yi-Ching},
++  title   = {Estimating the number of change-points via {S}chwarz' criterion},
++  journal = {Statistics \& Probability Letters},
++  year    = {1988},
++  volume  = {6},
++  number  = {3},
++  pages   = {181--189},
++  doi     = {10.1016/0167-7152(88)90118-6}
++}
++
++@misc{adams2007bocpd,
++  author        = {Adams, Ryan Prescott and MacKay, David J. C.},
++  title         = {Bayesian online changepoint detection},
++  year          = {2007},
++  howpublished  = {arXiv preprint arXiv:0710.3742},
++  note          = {Preprint}
++}
++
++@article{fearnhead2006exact,
++  author  = {Fearnhead, Paul},
++  title   = {Exact and efficient {B}ayesian inference for multiple changepoint problems},
++  journal = {Statistics and Computing},
++  year    = {2006},
++  volume  = {16},
++  number  = {2},
++  pages   = {203--213},
++  doi     = {10.1007/s11222-006-8450-8}
++}
++
++@article{bergmeir2012crossval,
++  author  = {Bergmeir, Christoph and Ben{\'i}tez, Jos{\'e} M.},
++  title   = {On the use of cross-validation for time series predictor evaluation},
++  journal = {Information Sciences},
++  year    = {2012},
++  volume  = {191},
++  pages   = {192--213},
++  doi     = {10.1016/j.ins.2011.12.028}
++}
++
++@article{friedman2001gbm,
++  author  = {Friedman, Jerome H.},
++  title   = {Greedy function approximation: A gradient boosting machine},
++  journal = {The Annals of Statistics},
++  year    = {2001},
++  volume  = {29},
++  number  = {5},
++  pages   = {1189--1232},
++  doi     = {10.1214/aos/1013203451}
++}
++
++@misc{pedregosa2011sklearn,
++  author        = {Pedregosa, Fabian and Varoquaux, Ga{\"e}l and Gramfort, Alexandre and Michel, Vincent and Thirion, Bertrand and Grisel, Olivier and Blondel, Mathieu and Prettenhofer, Peter and Weiss, Ron and Dubourg, Vincent and Vanderplas, Jake and Passos, Alexandre and Cournapeau, David and Brucher, Matthieu and Perrot, Matthieu and Duchesnay, {\'E}douard},
++  title         = {Scikit-learn: Machine learning in {P}ython},
++  year          = {2011},
++  howpublished  = {Journal of Machine Learning Research 12, 2825--2830; arXiv:1201.0490},
++  note          = {Verified through the arXiv record}
++}
++
++@article{hanley1982auc,
++  author  = {Hanley, James A. and McNeil, Barbara J.},
++  title   = {The meaning and use of the area under a receiver operating characteristic ({ROC}) curve},
++  journal = {Radiology},
++  year    = {1982},
++  volume  = {143},
++  number  = {1},
++  pages   = {29--36},
++  doi     = {10.1148/radiology.143.1.7063747}
++}
++
++@article{zhang2014multilabel,
++  author  = {Zhang, Min-Ling and Zhou, Zhi-Hua},
++  title   = {A review on multi-label learning algorithms},
++  journal = {IEEE Transactions on Knowledge and Data Engineering},
++  year    = {2014},
++  volume  = {26},
++  number  = {8},
++  pages   = {1819--1837},
++  doi     = {10.1109/TKDE.2013.39}
++}
++
++@article{cohen1960kappa,
++  author  = {Cohen, Jacob},
++  title   = {A coefficient of agreement for nominal scales},
++  journal = {Educational and Psychological Measurement},
++  year    = {1960},
++  volume  = {20},
++  number  = {1},
++  pages   = {37--46},
++  doi     = {10.1177/001316446002000104}
++}
++
++@article{fellegi1969linkage,
++  author  = {Fellegi, Ivan P. and Sunter, Alan B.},
++  title   = {A theory for record linkage},
++  journal = {Journal of the American Statistical Association},
++  year    = {1969},
++  volume  = {64},
++  number  = {328},
++  pages   = {1183--1210},
++  doi     = {10.1080/01621459.1969.10501049}
++}
++
++@article{efron1979bootstrap,
++  author  = {Efron, B.},
++  title   = {Bootstrap methods: Another look at the jackknife},
++  journal = {The Annals of Statistics},
++  year    = {1979},
++  volume  = {7},
++  number  = {1},
++  pages   = {1--26},
++  doi     = {10.1214/aos/1176344552}
++}
++
++@inproceedings{raasveldt2019duckdb,
++  author    = {Raasveldt, Mark and M{\"u}hleisen, Hannes},
++  title     = {{DuckDB}: an embeddable analytical database},
++  booktitle = {Proceedings of the 2019 International Conference on Management of Data (SIGMOD '19)},
++  year      = {2019},
++  pages     = {1981--1984},
++  doi       = {10.1145/3299869.3320212}
++}

added manuscript/references_verification_part02.csv +225 −0

++key,doi_or_arxiv,verified_via,verified_title,match,note
++priem2022openalex,arXiv:2205.01833,arxiv,"OpenAlex: A fully-open index of scholarly works, authors, venues, institutions, and concepts",exact,
++kinney2023s2,arXiv:2301.10140,arxiv,The Semantic Scholar Open Data Platform,exact,
++culbert2025openalex,arXiv:2401.16359,arxiv,Reference Coverage Analysis of OpenAlex compared to Web of Science and Scopus,exact,
++sutskever2014seq2seq,arXiv:1409.3215,arxiv,Sequence to Sequence Learning with Neural Networks,exact,
++bahdanau2015attention,arXiv:1409.0473,arxiv,Neural Machine Translation by Jointly Learning to Align and Translate,exact,
++goodfellow2014gan,arXiv:1406.2661,arxiv,Generative Adversarial Networks,exact,
++mikolov2013word2vec,arXiv:1301.3781,arxiv,Efficient Estimation of Word Representations in Vector Space,exact,
++kingma2014vae,arXiv:1312.6114,arxiv,Auto-Encoding Variational Bayes,exact,
++sevilla2022compute,arXiv:2202.05924,arxiv,Compute Trends Across Three Eras of Machine Learning,exact,
++thompson2020limits,arXiv:2007.05558,arxiv,The Computational Limits of Deep Learning,exact,
++ahmed2020dedemocratization,arXiv:2010.15581,arxiv,The De-democratization of AI: Deep Learning and the Compute Divide in Artificial Intelligence Research,exact,
++abadi2016tensorflow,arXiv:1605.08695,arxiv,TensorFlow: A system for large-scale machine learning,exact,
++paszke2019pytorch,arXiv:1912.01703,arxiv,"PyTorch: An Imperative Style, High-Performance Deep Learning Library",exact,
++vaswani2017attention,arXiv:1706.03762,arxiv,Attention Is All You Need,exact,
++brown2020gpt3,arXiv:2005.14165,arxiv,Language Models are Few-Shot Learners,exact,
++raffel2020t5,arXiv:1910.10683,arxiv,Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer,exact,
++kaplan2020scaling,arXiv:2001.08361,arxiv,Scaling Laws for Neural Language Models,exact,
++hoffmann2022chinchilla,arXiv:2203.15556,arxiv,Training Compute-Optimal Large Language Models,exact,
++chowdhery2022palm,arXiv:2204.02311,arxiv,PaLM: Scaling Language Modeling with Pathways,exact,
++fedus2022switch,arXiv:2101.03961,arxiv,Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity,exact,
++dosovitskiy2021vit,arXiv:2010.11929,arxiv,An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale,exact,
++radford2021clip,arXiv:2103.00020,arxiv,Learning Transferable Visual Models From Natural Language Supervision,exact,
++ramesh2021dalle,arXiv:2102.12092,arxiv,Zero-Shot Text-to-Image Generation,exact,
++ho2020ddpm,arXiv:2006.11239,arxiv,Denoising Diffusion Probabilistic Models,exact,
++sohl2015diffusion,arXiv:1503.03585,arxiv,Deep Unsupervised Learning using Nonequilibrium Thermodynamics,exact,
++song2021score,arXiv:2011.13456,arxiv,Score-Based Generative Modeling through Stochastic Differential Equations,exact,
++dhariwal2021beats,arXiv:2105.05233,arxiv,Diffusion Models Beat GANs on Image Synthesis,exact,
++bommasani2021foundation,arXiv:2108.07258,arxiv,On the Opportunities and Risks of Foundation Models,exact,
++wei2022cot,arXiv:2201.11903,arxiv,Chain-of-Thought Prompting Elicits Reasoning in Large Language Models,exact,
++wei2022emergent,arXiv:2206.07682,arxiv,Emergent Abilities of Large Language Models,exact,
++chen2021codex,arXiv:2107.03374,arxiv,Evaluating Large Language Models Trained on Code,exact,
++ouyang2022instructgpt,arXiv:2203.02155,arxiv,Training language models to follow instructions with human feedback,exact,
++christiano2017rlhf,arXiv:1706.03741,arxiv,Deep reinforcement learning from human preferences,exact,
++stiennon2020summarize,arXiv:2009.01325,arxiv,Learning to summarize from human feedback,exact,
++bai2022hh,arXiv:2204.05862,arxiv,Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback,exact,
++lewis2020rag,arXiv:2005.11401,arxiv,Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks,exact,
++hendrycks2021mmlu,arXiv:2009.03300,arxiv,Measuring Massive Multitask Language Understanding,exact,
++wang2019superglue,arXiv:1905.00537,arxiv,SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems,exact,
++srivastava2023bigbench,arXiv:2206.04615,arxiv,Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models,exact,
++patterson2021carbon,arXiv:2104.10350,arxiv,Carbon Emissions and Large Neural Network Training,exact,
++shoeybi2019megatron,arXiv:1909.08053,arxiv,Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism,exact,
++rajbhandari2020zero,arXiv:1910.02054,arxiv,ZeRO: Memory Optimizations Toward Training Trillion Parameter Models,exact,
++dao2022flashattention,arXiv:2205.14135,arxiv,FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness,exact,
++rae2021gopher,arXiv:2112.11446,arxiv,"Scaling Language Models: Methods, Analysis & Insights from Training Gopher",exact,
++bigscience2022bloom,arXiv:2211.05100,arxiv,BLOOM: A 176B-Parameter Open-Access Multilingual Language Model,exact,key scao2022bloom renamed bigscience2022bloom (first author / publication year)
++zhang2022opt,arXiv:2205.01068,arxiv,OPT: Open Pre-trained Transformer Language Models,exact,
++liu2019roberta,arXiv:1907.11692,arxiv,RoBERTa: A Robustly Optimized BERT Pretraining Approach,exact,
++chen2020simclr,arXiv:2002.05709,arxiv,A Simple Framework for Contrastive Learning of Visual Representations,exact,
++baevski2020wav2vec,arXiv:2006.11477,arxiv,wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations,exact,
++alayrac2022flamingo,arXiv:2204.14198,arxiv,Flamingo: a Visual Language Model for Few-Shot Learning,exact,
++reed2022gato,arXiv:2205.06175,arxiv,A Generalist Agent,exact,
++saharia2022imagen,arXiv:2205.11487,arxiv,Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding,exact,
++ramesh2022dalle2,arXiv:2204.06125,arxiv,Hierarchical Text-Conditional Image Generation with CLIP Latents,exact,
++jonas2019serverless,arXiv:1902.03383,arxiv,Cloud Programming Simplified: A Berkeley View on Serverless Computing,exact,
++hellerstein2019serverless,arXiv:1812.03651,arxiv,"Serverless Computing: One Step Forward, Two Steps Back",exact,
++chen2018tvm,arXiv:1802.04799,arxiv,TVM: An Automated End-to-End Optimizing Compiler for Deep Learning,exact,
++jacot2018ntk,arXiv:1806.07572,arxiv,Neural Tangent Kernel: Convergence and Generalization in Neural Networks,exact,
++lu2021codexglue,arXiv:2102.04664,arxiv,CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation,exact,
++szegedy2014intriguing,arXiv:1312.6199,arxiv,Intriguing properties of neural networks,exact,
++mcmahan2017fl,arXiv:1602.05629,arxiv,Communication-Efficient Learning of Deep Networks from Decentralized Data,exact,
++li2021fno,arXiv:2010.08895,arxiv,Fourier Neural Operator for Parametric Partial Differential Equations,exact,
++luccioni2023bloom,arXiv:2211.02001,arxiv,"Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Model",exact,
++levine2016visuomotor,arXiv:1504.00702,arxiv,End-to-End Training of Deep Visuomotor Policies,exact,
++openai2023gpt4,arXiv:2303.08774,arxiv,GPT-4 Technical Report,exact,
++touvron2023llama,arXiv:2302.13971,arxiv,LLaMA: Open and Efficient Foundation Language Models,exact,
++touvron2023llama2,arXiv:2307.09288,arxiv,Llama 2: Open Foundation and Fine-Tuned Chat Models,exact,
++grattafiori2024llama3,arXiv:2407.21783,arxiv,The Llama 3 Herd of Models,exact,key dubey2024llama3 renamed grattafiori2024llama3 (first author / publication year)
++bubeck2023sparks,arXiv:2303.12712,arxiv,Sparks of Artificial General Intelligence: Early experiments with GPT-4,exact,
++openai2024o1,arXiv:2412.16720,arxiv,OpenAI o1 System Card,exact,
++deepseek2025r1,arXiv:2501.12948,arxiv,DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning,exact,
++gu2023mamba,arXiv:2312.00752,arxiv,Mamba: Linear-Time Sequence Modeling with Selective State Spaces,exact,
++yao2023react,arXiv:2210.03629,arxiv,ReAct: Synergizing Reasoning and Acting in Language Models,exact,
++schick2023toolformer,arXiv:2302.04761,arxiv,Toolformer: Language Models Can Teach Themselves to Use Tools,exact,
++gemini2023,arXiv:2312.11805,arxiv,Gemini: A Family of Highly Capable Multimodal Models,exact,
++radford2023whisper,arXiv:2212.04356,arxiv,Robust Speech Recognition via Large-Scale Weak Supervision,exact,
++brohan2023rt2,arXiv:2307.15818,arxiv,RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control,exact,
++liang2024monitoring,arXiv:2403.07183,arxiv,Monitoring AI-Modified Content at Scale: A Case Study on the Impact of ChatGPT on AI Conference Peer Reviews,exact,
++liang2024mapping,arXiv:2404.01268,arxiv,Mapping the Increasing Use of LLMs in Scientific Papers,exact,
++geng2024writing,arXiv:2404.08627,arxiv,Is ChatGPT Transforming Academics' Writing Style?,exact,
++gray2024contamination,arXiv:2403.16887,arxiv,"ChatGPT ""contamination"": estimating the prevalence of LLMs in the scholarly literature",exact,
++ott2022benchmarks,arXiv:2203.04592,arxiv,Mapping global dynamics of benchmark creation and saturation in artificial intelligence,exact,
++dehghani2021lottery,arXiv:2107.07002,arxiv,The Benchmark Lottery,exact,
++raji2021benchmark,arXiv:2111.15366,arxiv,AI and the Everything in the Whole Wide World Benchmark,exact,
++maslej2024aiindex,arXiv:2405.19522,arxiv,Artificial Intelligence Index Report 2024,exact,
++maslej2025aiindex,arXiv:2504.07139,arxiv,Artificial Intelligence Index Report 2025,exact,
++pineau2021reproducibility,arXiv:2003.12206,arxiv,Improving Reproducibility in Machine Learning Research (A Report from the NeurIPS 2019 Reproducibility Program),exact,
++hendrycks2021unsolved,arXiv:2109.13916,arxiv,Unsolved Problems in ML Safety,exact,
++liang2025widespread,arXiv:2502.09747,arxiv,The Widespread Adoption of Large Language Model-Assisted Writing Across Society,exact,"key renamed: first author is Liang, not Kobak"
++ley2002dblp,10.1007/3-540-45735-6_1,crossref,"The DBLP Computer Science Bibliography: Evolution, Research Issues, Perspectives",exact,
++ginsparg2011arxiv,10.1038/476145a,crossref,ArXiv at 20,exact,
++wang2020mag,10.1162/qss_a_00021,crossref,Microsoft Academic Graph: When experts are not enough,exact,
++waltman2012classification,10.1002/asi.22748,crossref,A new methodology for constructing a publication‐level classification system of science,exact,
++glanzel2003classification,10.1023/A:1022378804087,crossref,A new classification scheme of science fields and subfields designed for scientometric evaluation purposes,exact,
++klavans2017taxonomy,10.1002/asi.23734,crossref,Which Type of Citation Analysis Generates the Most Accurate Taxonomy of Scientific and Technical Knowledge?,exact,
++sjogarde2018granularity,10.1016/j.joi.2017.12.006,crossref,Granularity of algorithmically constructed publication-level classifications of research publications: Identification of topics,exact,
++traag2019leiden,10.1038/s41598-019-41695-z,crossref,From Louvain to Leiden: guaranteeing well-connected communities,exact,
++rous2012ccs,10.1145/2366316.2366320,crossref,Major update to ACM's Computing Classification System,exact,
++vardi2009conferences,10.1145/1506409.1506410,crossref,Conferences vs. journals in computing research,exact,
++fortnow2009growup,10.1145/1536616.1536631,crossref,ViewpointTime for computer science to grow up,minor,Crossref title concatenates 'Viewpoint' label; record matches
++lin2020preprints,10.1007/s11192-020-03430-8,crossref,How many preprints have actually been printed and why: a case study of computer science preprints on arXiv,exact,
++denning2005science,10.1145/1053291.1053309,crossref,Is computer science science?,exact,
++wing2006computational,10.1145/1118178.1118215,crossref,Computational thinking,exact,
++bornmann2021growth,10.1057/s41599-021-00903-w,crossref,Growth rates of modern science: a latent piecewise growth curve approach to model publication numbers from established and new literature databases,exact,
++visser2021comparison,10.1162/qss_a_00112,crossref,"Large-scale comparison of bibliographic data sources: Scopus, Web of Science, Dimensions, Crossref, and Microsoft Academic",exact,
++martinmartin2021coverage,10.1007/s11192-020-03690-4,crossref,"Google Scholar, Microsoft Academic, Scopus, Dimensions, Web of Science, and OpenCitations’ COCI: a multidisciplinary comparison of coverage via citations",exact,
++bernerslee1994www,10.1145/179606.179671,crossref,The World-Wide Web,exact,
++brin1998anatomy,10.1016/S0169-7552(98)00110-X,crossref,The anatomy of a large-scale hypertextual Web search engine,exact,
++dean2008mapreduce,10.1145/1327452.1327492,crossref,MapReduce: simplified data processing on large clusters,exact,title+subtitle fields
++armbrust2010cloud,10.1145/1721654.1721672,crossref,A view of cloud computing,exact,
++ghemawat2003gfs,10.1145/945445.945450,crossref,The Google file system,exact,
++kleinberg1999hubs,10.1145/324133.324140,crossref,Authoritative sources in a hyperlinked environment,exact,
++krizhevsky2017imagenet,10.1145/3065386,crossref,ImageNet classification with deep convolutional neural networks,exact,
++deng2009imagenet,10.1109/CVPR.2009.5206848,crossref,ImageNet: A large-scale hierarchical image database,exact,
++russakovsky2015imagenet,10.1007/s11263-015-0816-y,crossref,ImageNet Large Scale Visual Recognition Challenge,exact,
++he2016resnet,10.1109/CVPR.2016.90,crossref,Deep Residual Learning for Image Recognition,exact,
++lecun2015deep,10.1038/nature14539,crossref,Deep learning,exact,
++silver2016alphago,10.1038/nature16961,crossref,Mastering the game of Go with deep neural networks and tree search,exact,
++silver2017alphagozero,10.1038/nature24270,crossref,Mastering the game of Go without human knowledge,exact,
++silver2018alphazero,10.1126/science.aar6404,crossref,"A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play",exact,
++mnih2015dqn,10.1038/nature14236,crossref,Human-level control through deep reinforcement learning,exact,
++hochreiter1997lstm,10.1162/neco.1997.9.8.1735,crossref,Long Short-Term Memory,exact,
++hinton2012speech,10.1109/MSP.2012.2205597,crossref,Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups,exact,title+subtitle fields
++lipton2019troubling,10.1145/3317287.3328534,crossref,Troubling Trends in Machine Learning Scholarship,exact,
++ahmed2023industry,10.1126/science.ade2420,crossref,The growing influence of industry in AI research,exact,
++wolf2020transformers,10.18653/v1/2020.emnlp-demos.6,crossref,Transformers: State-of-the-Art Natural Language Processing,exact,
++vinyals2019alphastar,10.1038/s41586-019-1724-z,crossref,Grandmaster level in StarCraft II using multi-agent reinforcement learning,exact,
++devlin2019bert,10.18653/v1/N19-1423,crossref,BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding,exact,
++peters2018elmo,10.18653/v1/N18-1202,crossref,Deep Contextualized Word Representations,exact,
++rombach2022ldm,10.1109/CVPR52688.2022.01042,crossref,High-Resolution Image Synthesis with Latent Diffusion Models,exact,
++jumper2021alphafold,10.1038/s41586-021-03819-2,crossref,Highly accurate protein structure prediction with AlphaFold,exact,
++bender2021parrots,10.1145/3442188.3445922,crossref,On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?,exact,title+subtitle fields (Crossref title ends with a parrot emoji)
++wang2018glue,10.18653/v1/W18-5446,crossref,GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding,exact,
++rajpurkar2016squad,10.18653/v1/D16-1264,crossref,"SQuAD: 100,000+ Questions for Machine Comprehension of Text",exact,
++strubell2019energy,10.18653/v1/P19-1355,crossref,Energy and Policy Considerations for Deep Learning in NLP,exact,
++schwartz2020green,10.1145/3381831,crossref,Green AI,exact,
++he2022mae,10.1109/CVPR52688.2022.01553,crossref,Masked Autoencoders Are Scalable Vision Learners,exact,
++caron2021dino,10.1109/ICCV48922.2021.00951,crossref,Emerging Properties in Self-Supervised Vision Transformers,exact,
++dean2013tail,10.1145/2408776.2408794,crossref,The tail at scale,exact,
++barroso2018datacenter,10.2200/S00874ED3V01Y201809CAC046,openalex,"The Datacenter as a Computer: Designing Warehouse-Scale Machines, Third Edition",exact,crossref timed out; verified via OpenAlex
++lim2009disaggregated,10.1145/1555754.1555789,crossref,Disaggregated memory for expansion and sharing in blade servers,exact,
++zaharia2016spark,10.1145/2934664,crossref,Apache Spark: a unified engine for big data processing,exact,title+subtitle fields
++hennessy2019golden,10.1145/3282307,crossref,A new golden age for computer architecture,exact,
++esmaeilzadeh2011dark,10.1145/2000064.2000108,crossref,Dark silicon and the end of multicore scaling,exact,
++jouppi2017tpu,10.1145/3079856.3080246,crossref,In-Datacenter Performance Analysis of a Tensor Processing Unit,exact,
++leiserson2020room,10.1126/science.aam9744,crossref,There’s plenty of room at the Top: What will drive computer performance after Moore’s law?,exact,
++dennard1974scaling,10.1109/JSSC.1974.1050511,crossref,Design of ion-implanted MOSFET's with very small physical dimensions,exact,
++lattner2021mlir,10.1109/CGO51591.2021.9370308,crossref,MLIR: Scaling Compiler Infrastructure for Domain Specific Computation,exact,
++williams2018finegrained,10.1142/9789813272880_0188,crossref,ON SOME FINE-GRAINED QUESTIONS IN ALGORITHMS AND COMPLEXITY,exact,
++vassilevska2015hardness,10.4230/LIPIcs.IPEC.2015.17,openalex,Hardness of Easy Problems: Basing Hardness on Popular Conjectures such as the Strong Exponential Time Hypothesis (Invited Talk),minor,OpenAlex title adds '(Invited Talk)'
++zhang2021rethinking,10.1145/3446776,crossref,Understanding deep learning (still) requires rethinking generalization,exact,
++belkin2019reconciling,10.1073/pnas.1903070116,crossref,Reconciling modern machine-learning practice and the classical bias–variance trade-off,exact,
++preskill2018nisq,10.22331/q-2018-08-06-79,crossref,Quantum Computing in the NISQ era and beyond,exact,
++arute2019supremacy,10.1038/s41586-019-1666-5,crossref,Quantum supremacy using a programmable superconducting processor,exact,
++bharti2022nisq,10.1103/RevModPhys.94.015004,crossref,Noisy intermediate-scale quantum algorithms,exact,
++google2023surface,10.1038/s41586-022-05434-1,crossref,Suppressing quantum errors by scaling a surface code logical qubit,exact,
++google2025threshold,10.1038/s41586-024-08449-y,crossref,Quantum error correction below the surface code threshold,exact,key google2024threshold renamed google2025threshold (first author / publication year)
++jung2017rustbelt,10.1145/3158154,crossref,RustBelt: securing the foundations of the Rust programming language,exact,
++matsakis2014rust,10.1145/2692956.2663188,crossref,The rust language,exact,
++leroy2009compcert,10.1145/1538788.1538814,crossref,Formal verification of a realistic compiler,exact,
++klein2009sel4,10.1145/1629575.1629596,crossref,seL4: formal verification of an OS kernel,exact,title+subtitle fields
++demoura2015lean,10.1007/978-3-319-21401-6_26,crossref,The Lean Theorem Prover (System Description),exact,
++demoura2021lean4,10.1007/978-3-030-79876-5_37,crossref,The Lean 4 Theorem Prover and Programming Language,exact,
++mathlib2020,10.1145/3372885.3373824,crossref,The lean mathematical library,exact,
++hales2017kepler,10.1017/fmp.2017.1,crossref,A FORMAL PROOF OF THE KEPLER CONJECTURE,exact,
++allamanis2018survey,10.1145/3212695,crossref,A Survey of Machine Learning for Big Code and Naturalness,exact,
++bernstein2017pqc,10.1038/nature23461,crossref,Post-quantum cryptography,exact,
++shor1997algorithms,10.1137/S0097539795293172,crossref,Polynomial-Time Algorithms for Prime Factorization and Discrete Logarithms on a Quantum Computer,exact,
++nist2024fips203,10.6028/NIST.FIPS.203,crossref,Module-lattice-based key-encapsulation mechanism standard,exact,
++nist2024fips204,10.6028/NIST.FIPS.204,crossref,Module-lattice-based digital signature standard,exact,
++nist2024fips205,10.6028/NIST.FIPS.205,crossref,Stateless hash-based digital signature standard,exact,
++kocher2019spectre,10.1109/SP.2019.00002,crossref,Spectre Attacks: Exploiting Speculative Execution,exact,
++lipp2020meltdown,10.1145/3357033,crossref,Meltdown: reading kernel memory from user space,exact,title+subtitle fields
++biggio2018wild,10.1016/j.patcog.2018.07.023,crossref,Wild patterns: Ten years after the rise of adversarial machine learning,exact,
++carlini2017robustness,10.1109/SP.2017.49,crossref,Towards Evaluating the Robustness of Neural Networks,exact,
++dwork2006calibrating,10.1007/11681878_14,crossref,Calibrating Noise to Sensitivity in Private Data Analysis,exact,
++dwork2014algorithmic,10.1561/0400000042,crossref,The Algorithmic Foundations of Differential Privacy,exact,
++abadi2016dpsgd,10.1145/2976749.2978318,crossref,Deep Learning with Differential Privacy,exact,
++kairouz2021fl,10.1561/2200000083,crossref,Advances and Open Problems in Federated Learning,exact,
++gentry2009fhe,10.1145/1536414.1536440,crossref,Fully homomorphic encryption using ideal lattices,exact,
++bonneau2015sok,10.1109/SP.2015.14,crossref,SoK: Research Perspectives and Challenges for Bitcoin and Cryptocurrencies,exact,
++amershi2019guidelines,10.1145/3290605.3300233,crossref,Guidelines for Human-AI Interaction,exact,
++shneiderman2020hcai,10.1080/10447318.2020.1741118,crossref,"Human-Centered Artificial Intelligence: Reliable, Safe & Trustworthy",exact,title+subtitle fields
++yang2020reexamining,10.1145/3313831.3376301,crossref,"Re-examining Whether, Why, and How Human-AI Interaction Is Uniquely Difficult to Design",exact,
++mildenhall2020nerf,10.1007/978-3-030-58452-8_24,crossref,NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis,exact,
++tewari2020neural,10.1111/cgf.14022,crossref,State of the Art on Neural Rendering,exact,
++kerbl2023gaussian,10.1145/3592433,crossref,3D Gaussian Splatting for Real-Time Radiance Field Rendering,exact,
++baek2021rosettafold,10.1126/science.abj8754,crossref,Accurate prediction of protein structures and interactions using a three-track neural network,exact,
++senior2020alphafold,10.1038/s41586-019-1923-7,crossref,Improved protein structure prediction using potentials from deep learning,exact,
++lin2023esm,10.1126/science.ade2574,crossref,Evolutionary-scale prediction of atomic-level protein structure with a language model,exact,
++abramson2024af3,10.1038/s41586-024-07487-w,crossref,Accurate structure prediction of biomolecular interactions with AlphaFold 3,exact,
++bosshart2014p4,10.1145/2656877.2656890,crossref,P4: programming protocol-independent packet processors,exact,title+subtitle fields
++mckeown2008openflow,10.1145/1355734.1355746,crossref,OpenFlow: enabling innovation in campus networks,exact,title+subtitle fields
++langley2017quic,10.1145/3098822.3098842,crossref,The QUIC Transport Protocol: Design and Internet-Scale Deployment,exact,title+subtitle fields
++shi2016edge,10.1109/JIOT.2016.2579198,crossref,Edge Computing: Vision and Challenges,exact,
++kraska2018learned,10.1145/3183713.3196909,crossref,The Case for Learned Index Structures,exact,
++malkov2020hnsw,10.1109/TPAMI.2018.2889473,crossref,Efficient and Robust Approximate Nearest Neighbor Search Using Hierarchical Navigable Small World Graphs,exact,
++johnson2021faiss,10.1109/TBDATA.2019.2921572,crossref,Billion-Scale Similarity Search with GPUs,exact,
++karpukhin2020dpr,10.18653/v1/2020.emnlp-main.550,crossref,Dense Passage Retrieval for Open-Domain Question Answering,exact,
++raissi2019pinn,10.1016/j.jcp.2018.10.045,crossref,Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations,exact,
++karniadakis2021piml,10.1038/s42254-021-00314-5,crossref,Physics-informed machine learning,exact,
++wang2023aiscience,10.1038/s41586-023-06221-2,crossref,Scientific discovery in the age of artificial intelligence,exact,
++masanet2020datacenter,10.1126/science.aba3758,crossref,Recalibrating global data center energy-use estimates,exact,
++bai1998breaks,10.2307/2998540,crossref,Estimating and Testing Linear Models with Multiple Structural Changes,exact,
++bai2003computation,10.1002/jae.659,crossref,Computation and analysis of multiple structural change models,exact,
++bernal2017its,10.1093/ije/dyw098,crossref,Interrupted time series regression for the evaluation of public health interventions: a tutorial,exact,
++andrews1993tests,10.2307/2951764,crossref,Tests for Parameter Instability and Structural Change With Unknown Change Point,exact,
++killick2012pelt,10.1080/01621459.2012.737745,crossref,Optimal Detection of Changepoints With a Linear Computational Cost,exact,
++kobak2025delving,10.1126/sciadv.adt3813,crossref,Delving into LLM-assisted writing in biomedical publications through excess vocabulary,exact,
++dwivedi2023sowhat,10.1016/j.ijinfomgt.2023.102642,crossref,"Opinion Paper: “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy",minor,Crossref title prefixed 'Opinion Paper:'; record matches
++vannoorden2023survey,10.1038/d41586-023-02980-0,crossref,"AI and science: what 1,600 researchers think",exact,
++messeri2024illusions,10.1038/s41586-024-07146-0,crossref,Artificial intelligence and illusions of understanding in scientific research,exact,
++birhane2023science,10.1038/s42254-023-00581-4,crossref,Science in the age of large language models,exact,
++krenn2023forecasting,10.1038/s42256-023-00735-0,crossref,Forecasting the future of artificial intelligence with machine learning-based link prediction in an exponentially growing knowledge network,exact,
++fortunato2018sos,10.1126/science.aao0185,crossref,Science of science,exact,
++wang2021sos,10.1017/9781108610834,crossref,The Science of Science,exact,
++park2023disruptive,10.1038/s41586-022-05543-x,crossref,Papers and patents are becoming less disruptive over time,exact,
++chu2021slowed,10.1073/pnas.2021636118,crossref,Slowed canonical progress in large fields of science,exact,
++bornmann2015growth,10.1002/asi.23329,crossref,Growth rates of modern science: A bibliometric analysis based on the number of publications and cited references,exact,
++binnashwan2023chatgpt,10.1016/j.techsoc.2023.102370,crossref,Use of ChatGPT in academia: Academic integrity hangs in the balance,exact,
++kapoor2023leakage,10.1016/j.patter.2023.100804,crossref,Leakage and the reproducibility crisis in machine-learning-based science,exact,
++gundersen2018reproducibility,10.1609/aaai.v32i1.11503,crossref,State of the Art: Reproducibility in Artificial Intelligence,exact,
++openai2022chatgpt,https://openai.com/blog/chatgpt,url,Introducing ChatGPT,exact,Wayback snapshot 2022-12-02 (status 200); live URL openai.com/index/chatgpt/ snapshot 2025-01-02
++openalex2025topics,https://help.openalex.org/data/topics/,url,Topics (OpenAlex documentation),exact,HTTP 200 on 2026-11-03
++arxiv2026stats,https://arxiv.org/stats/monthly_submissions,url,arXiv monthly submissions,exact,HTTP 200 on 2026-11-03

modified manuscript/references_verification_part03.csv +34 −34

				`@@ -1,34 +1,34 @@`
1	−key,doi_or_arxiv,verified_via,verified_title,match	1	+key,doi_or_arxiv,verified_via,verified_title,match,note
2	−page2021prisma,10.1136/bmj.n71,crossref,"The PRISMA 2020 statement: an updated guideline for reporting systematic reviews",exact	2	+page2021prisma,10.1136/bmj.n71,crossref,"The PRISMA 2020 statement: an updated guideline for reporting systematic reviews",exact,reused:references.bib (same key and DOI; defined there)
3	−page2021prismaee,10.1136/bmj.n160,crossref,"PRISMA 2020 explanation and elaboration: updated guidance and exemplars for reporting systematic reviews",exact	3	+page2021prismaee,10.1136/bmj.n160,crossref,"PRISMA 2020 explanation and elaboration: updated guidance and exemplars for reporting systematic reviews",exact,defined in references_part03.bib
4	−petersen2008mapping,10.14236/ewic/EASE2008.8,crossref,"Systematic Mapping Studies in Software Engineering",exact	4	+petersen2008mapping,10.14236/ewic/EASE2008.8,crossref,"Systematic Mapping Studies in Software Engineering",exact,reused:references.bib (same key and DOI; defined there)
5	−petersen2015mapping,10.1016/j.infsof.2015.03.007,crossref,"Guidelines for conducting systematic mapping studies in software engineering: An update",exact	5	+petersen2015mapping,10.1016/j.infsof.2015.03.007,crossref,"Guidelines for conducting systematic mapping studies in software engineering: An update",exact,reused:references.bib (same key and DOI; defined there)
6	−tricco2018prismascr,10.7326/M18-0850,crossref,"PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation",exact	6	+tricco2018prismascr,10.7326/M18-0850,crossref,"PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation",exact,reused:references.bib (same key and DOI; defined there)
7	−rethlefsen2021prismas,10.1186/s13643-020-01542-z,crossref,"PRISMA-S: an extension to the PRISMA Statement for Reporting Literature Searches in Systematic Reviews",exact	7	+rethlefsen2021prismas,10.1186/s13643-020-01542-z,crossref,"PRISMA-S: an extension to the PRISMA Statement for Reporting Literature Searches in Systematic Reviews",exact,reused:references.bib (same key and DOI; defined there)
8	−haddaway2022prismaflow,10.1002/cl2.1230,crossref,"PRISMA2020: An R package and Shiny app for producing PRISMA 2020-compliant flow diagrams, with interactivity for optimised digital transparency and Open Synthesis",exact	8	+haddaway2022prismaflow,10.1002/cl2.1230,crossref,"PRISMA2020: An R package and Shiny app for producing PRISMA 2020-compliant flow diagrams, with interactivity for optimised digital transparency and Open Synthesis",exact,reused:references.bib (same key and DOI; defined there)
9	−gusenbauer2020search,10.1002/jrsm.1378,crossref,"Which academic search systems are suitable for systematic reviews or meta-analyses? Evaluating retrieval qualities of Google Scholar, PubMed, and 26 other resources",exact	9	+gusenbauer2020search,10.1002/jrsm.1378,crossref,"Which academic search systems are suitable for systematic reviews or meta-analyses? Evaluating retrieval qualities of Google Scholar, PubMed, and 26 other resources",exact,defined in references_part03.bib
10	−ley2002dblp,10.1007/3-540-45735-6_1,crossref,"The DBLP Computer Science Bibliography: Evolution, Research Issues, Perspectives",exact	10	+ley2002dblp,10.1007/3-540-45735-6_1,crossref,"The DBLP Computer Science Bibliography: Evolution, Research Issues, Perspectives",exact,reused:references.bib (same key and DOI; defined there)
11	−ley2009dblp,10.14778/1687553.1687577,crossref,"DBLP (Proceedings of the VLDB Endowment 2(2) 1493-1500; full title 'DBLP: some lessons learned' truncated by Crossref)",minor	11	+ley2009dblp,10.14778/1687553.1687577,crossref,"DBLP (Proceedings of the VLDB Endowment 2(2) 1493-1500; full title 'DBLP: some lessons learned' truncated by Crossref)",minor,reused:references.bib (same key and DOI; defined there)
12	−ginsparg2011arxiv,10.1038/476145a,crossref,"ArXiv at 20",exact	12	+ginsparg2011arxiv,10.1038/476145a,crossref,"ArXiv at 20",exact,reused:references.bib (same key and DOI; defined there)
13	−clement2019arxiv,arXiv:1905.00075,arxiv,"On the Use of ArXiv as a Dataset",exact	13	+clement2019arxiv,arXiv:1905.00075,arxiv,"On the Use of ArXiv as a Dataset",exact,defined in references_part03.bib
14	−priem2022openalex,arXiv:2205.01833,arxiv,"OpenAlex: A fully-open index of scholarly works, authors, venues, institutions, and concepts",exact	14	+priem2022openalex,arXiv:2205.01833,arxiv,"OpenAlex: A fully-open index of scholarly works, authors, venues, institutions, and concepts",exact,reused:references.bib (same key and DOI; defined there)
15	−kinney2023s2,arXiv:2301.10140,arxiv,"The Semantic Scholar Open Data Platform",exact	15	+kinney2023s2,arXiv:2301.10140,arxiv,"The Semantic Scholar Open Data Platform",exact,defined in references_part03.bib
16	−alperin2024openalex,arXiv:2404.17663,arxiv,"An analysis of the suitability of OpenAlex for bibliometric analyses",exact	16	+alperin2024openalex,arXiv:2404.17663,arxiv,"An analysis of the suitability of OpenAlex for bibliometric analyses",exact,reused:references.bib (same key and DOI; defined there)
17	−culbert2025openalex,10.1007/s11192-025-05293-3,crossref,"Reference coverage analysis of OpenAlex compared to Web of Science and Scopus",exact	17	+culbert2025openalex,10.1007/s11192-025-05293-3,crossref,"Reference coverage analysis of OpenAlex compared to Web of Science and Scopus",exact,defined in references_part03.bib
18	−scheidsteger2023openalex,10.3145/epi.2023.mar.09,crossref,"Which of the metadata with relevance for bibliometrics are the same and which are different when switching from Microsoft Academic Graph to OpenAlex?",exact	18	+scheidsteger2023openalex,10.3145/epi.2023.mar.09,crossref,"Which of the metadata with relevance for bibliometrics are the same and which are different when switching from Microsoft Academic Graph to OpenAlex?",exact,defined in references_part03.bib
19	−delgadoquiros2024completeness,10.1162/qss_a_00286,crossref,"Completeness degree of publication metadata in eight free-access scholarly databases",exact	19	+delgadoquiros2024completeness,10.1162/qss_a_00286,crossref,"Completeness degree of publication metadata in eight free-access scholarly databases",exact,defined in references_part03.bib
20	−visser2021comparison,10.1162/qss_a_00112,crossref,"Large-scale comparison of bibliographic data sources: Scopus, Web of Science, Dimensions, Crossref, and Microsoft Academic",exact	20	+visser2021comparison,10.1162/qss_a_00112,crossref,"Large-scale comparison of bibliographic data sources: Scopus, Web of Science, Dimensions, Crossref, and Microsoft Academic",exact,reused:references.bib (same key and DOI; defined there)
21	−martinmartin2021comparison,10.1007/s11192-020-03690-4,crossref,"Google Scholar, Microsoft Academic, Scopus, Dimensions, Web of Science, and OpenCitations' COCI: a multidisciplinary comparison of coverage via citations",exact	21	+martinmartin2021comparison,10.1007/s11192-020-03690-4,crossref,"Google Scholar, Microsoft Academic, Scopus, Dimensions, Web of Science, and OpenCitations' COCI: a multidisciplinary comparison of coverage via citations",exact,defined in references_part03.bib
22	−mongeon2016coverage,10.1007/s11192-015-1765-5,crossref,"The journal coverage of Web of Science and Scopus: a comparative analysis",exact	22	+mongeon2016coverage,10.1007/s11192-015-1765-5,crossref,"The journal coverage of Web of Science and Scopus: a comparative analysis",exact,defined in references_part03.bib
23	−hendricks2020crossref,10.1162/qss_a_00022,crossref,"Crossref: The sustainable source of community-owned scholarly metadata",exact	23	+hendricks2020crossref,10.1162/qss_a_00022,crossref,"Crossref: The sustainable source of community-owned scholarly metadata",exact,reused:references.bib (same key and DOI; defined there)
24	−wang2020mag,10.1162/qss_a_00021,crossref,"Microsoft Academic Graph: When experts are not enough",exact	24	+wang2020mag,10.1162/qss_a_00021,crossref,"Microsoft Academic Graph: When experts are not enough",exact,reused:references.bib (same key and DOI; defined there)
25	−radev2013acl,10.1007/s10579-012-9211-2,crossref,"The ACL anthology network corpus",exact	25	+radev2013acl,10.1007/s10579-012-9211-2,crossref,"The ACL anthology network corpus",exact,defined in references_part03.bib
26	−fellegi1969linkage,10.1080/01621459.1969.10501049,crossref,"A Theory for Record Linkage",exact	26	+fellegi1969linkage,10.1080/01621459.1969.10501049,crossref,"A Theory for Record Linkage",exact,defined in references_part03.bib
27	−christen2012matching,10.1007/978-3-642-31164-2,crossref,"Data Matching",exact	27	+christen2012matching,10.1007/978-3-642-31164-2,crossref,"Data Matching",exact,defined in references_part03.bib
28	−lin2020preprints,10.1007/s11192-020-03430-8,crossref,"How many preprints have actually been printed and why: a case study of computer science preprints on arXiv",exact	28	+lin2020preprints,10.1007/s11192-020-03430-8,crossref,"How many preprints have actually been printed and why: a case study of computer science preprints on arXiv",exact,defined in references_part03.bib
29	−cabanac2021preprint,10.1007/s11192-021-03900-7,crossref,"Day-to-day discovery of preprint–publication links",exact	29	+cabanac2021preprint,10.1007/s11192-021-03900-7,crossref,"Day-to-day discovery of preprint–publication links",exact,defined in references_part03.bib
30	−wang2013window,10.1007/s11192-012-0775-9,crossref,"Citation time window choice for research impact evaluation",exact	30	+wang2013window,10.1007/s11192-012-0775-9,crossref,"Citation time window choice for research impact evaluation",exact,defined in references_part03.bib
31	−waltman2016review,10.1016/j.joi.2016.02.007,crossref,"A review of the literature on citation impact indicators",exact	31	+waltman2016review,10.1016/j.joi.2016.02.007,crossref,"A review of the literature on citation impact indicators",exact,reused:references.bib (same key and DOI; defined there)
32	−fortunato2018science,10.1126/science.aao0185,crossref,"Science of science",exact	32	+fortunato2018science,10.1126/science.aao0185,crossref,"Science of science",exact,defined in references_part03.bib
33	−nosek2018preregistration,10.1073/pnas.1708274114,crossref,"The preregistration revolution",exact	33	+nosek2018preregistration,10.1073/pnas.1708274114,crossref,"The preregistration revolution",exact,defined in references_part03.bib
34	−vannoorden2023retractions,10.1038/d41586-023-03974-8,crossref,"More than 10,000 research papers were retracted in 2023 — a new record",exact	34	+vannoorden2023retractions,10.1038/d41586-023-03974-8,crossref,"More than 10,000 research papers were retracted in 2023 — a new record",exact,defined in references_part03.bib

added manuscript/references_verification_part04.csv +48 −0

++key,doi_or_arxiv,verified_via,verified_title,match
++waltman2016review,10.1016/j.joi.2016.02.007,crossref,A review of the literature on citation impact indicators,yes
++waltman2019fieldnorm,10.1007/978-3-030-02511-3_11,crossref,Field Normalization of Scientometric Indicators,yes
++wang2013window,10.1007/s11192-012-0775-9,crossref,Citation time window choice for research impact evaluation,yes (Crossref issue date 2012; printed volume 94(3) 2013)
++openalex2026fwci,https://help.openalex.org/data/works/attributes/,url HTTP 200 (redirect from docs.openalex.org/api-entities/works/work-object),"fwci: citations received divided by citations expected for works of the same type, year, and subfield",yes (documentation page; definition quoted)
++ceriani2012gini,10.1007/s10888-011-9188-x,crossref,The origins of the Gini index: extracts from Variabilita e Mutabilita (1912) by Corrado Gini,yes (online 2011; volume 10(3) 2012)
++hirschman1964paternity,OpenAlex W2163989766 (no DOI),openalex,THE PATERNITY OF AN INDEX,yes (American Economic Review 54(5) 761-762)
++rao1982diversity,10.1016/0040-5809(82)90004-1,crossref,Diversity and dissimilarity coefficients: A unified approach,yes
++stirling2007diversity,10.1098/rsif.2007.0213,crossref,"A general framework for analysing diversity in science, technology and society",yes
++leinster2012diversity,10.1890/10-2402.1,crossref,Measuring diversity: the importance of species similarity,yes
++rafols2010diversity,10.1007/s11192-009-0041-y,crossref,Diversity and network coherence as indicators of interdisciplinarity: case studies in bionanoscience,yes (online 2009; volume 82(2) 2010)
++zhang2016diversity,10.1002/asi.23487,crossref,Diversity of references as an indicator of the interdisciplinarity of journals: Taking similarity between subject fields into account,yes (online 2015; volume 67(5) 2016)
++newman2004community,10.1103/PhysRevE.69.026113,crossref,Finding and evaluating community structure in networks,yes
++leicht2008directed,10.1103/PhysRevLett.100.118703,crossref,Community Structure in Directed Networks,yes
++fortunato2007resolution,10.1073/pnas.0605965104,crossref,Resolution limit in community detection,yes
++fortunato2010community,10.1016/j.physrep.2009.11.002,crossref,Community detection in graphs,yes
++cohan2020specter,10.18653/v1/2020.acl-main.207,crossref,SPECTER: Document-level Representation Learning using Citation-informed Transformers,yes
++singh2023scirepeval,10.18653/v1/2023.emnlp-main.338,crossref (DOI found via OpenAlex W4389524502),SciRepEval: A Multi-Format Benchmark for Scientific Document Representations,yes
++ostendorff2022scincl,10.18653/v1/2022.emnlp-main.802,crossref,Neighborhood Contrastive Learning for Scientific Document Representations with Citation Embeddings,yes
++reimers2019sbert,10.18653/v1/D19-1410,crossref,Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks,yes
++mcinnes2018umap,arXiv:1802.03426,arxiv,UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction,yes (preprint)
++mcinnes2017hdbscan,10.21105/joss.00205,crossref,hdbscan: Hierarchical density based clustering,yes
++campello2013hdbscan,10.1007/978-3-642-37456-2_14,crossref,Density-Based Clustering Based on Hierarchical Density Estimates,yes
++hubert1985comparing,10.1007/BF01908075,crossref,Comparing partitions,yes
++uzzi2013atypical,10.1126/science.1240474,crossref,Atypical Combinations and Scientific Impact,yes
++wang2017novelty,10.1016/j.respol.2017.06.006,crossref,Bias against novelty in science: A cautionary tale for users of bibliometric indicators,yes
++bernal2017its,10.1093/ije/dyw098,crossref,Interrupted time series regression for the evaluation of public health interventions: a tutorial,yes (online 2016; volume 46(1) 2017)
++bernal2018controls,10.1093/ije/dyy135,crossref,The use of controls in interrupted time series studies of public health interventions,yes
++newey1987hac,10.2307/1913610,crossref,"A Simple, Positive Semi-Definite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix",yes
++andrews1991hac,10.2307/2938229,crossref,Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation,yes
++bai1998breaks,10.2307/2998540,crossref,Estimating and Testing Linear Models with Multiple Structural Changes,yes
++bai2003computation,10.1002/jae.659,crossref,Computation and analysis of multiple structural change models,yes (Crossref issue date 2002; volume 18(1) 2003)
++liu1997segmented,OpenAlex W2142183678 (no DOI),openalex,ON SEGMENTED MULTIVARIATE REGRESSION,yes (authors Liu/Wu/Zidek 1997; venue Statistica Sinica not in OpenAlex record)
++schwarz1978bic,10.1214/aos/1176344136,crossref,Estimating the Dimension of a Model,yes
++killick2012pelt,10.1080/01621459.2012.737745,crossref,Optimal Detection of Changepoints With a Linear Computational Cost,yes
++truong2020ruptures,10.1016/j.sigpro.2019.107299,crossref,Selective review of offline change point detection methods,yes
++yao1988schwarz,10.1016/0167-7152(88)90118-6,crossref,Estimating the number of change-points via Schwarz' criterion,yes
++adams2007bocpd,arXiv:0710.3742,arxiv,Bayesian Online Changepoint Detection,yes (preprint)
++fearnhead2006exact,10.1007/s11222-006-8450-8,crossref,Exact and efficient Bayesian inference for multiple changepoint problems,yes
++bergmeir2012crossval,10.1016/j.ins.2011.12.028,crossref,On the use of cross-validation for time series predictor evaluation,yes
++friedman2001gbm,10.1214/aos/1013203451,crossref,Greedy function approximation: A gradient boosting machine.,yes
++pedregosa2011sklearn,arXiv:1201.0490,arxiv,Scikit-learn: Machine Learning in Python,yes (JMLR 12 2011 version cited; arXiv record verified)
++hanley1982auc,10.1148/radiology.143.1.7063747,crossref,The meaning and use of the area under a receiver operating characteristic (ROC) curve.,yes
++zhang2014multilabel,10.1109/TKDE.2013.39,crossref,A Review on Multi-Label Learning Algorithms,yes
++cohen1960kappa,10.1177/001316446002000104,crossref,A Coefficient of Agreement for Nominal Scales,yes
++fellegi1969linkage,10.1080/01621459.1969.10501049,crossref,A Theory for Record Linkage,yes
++efron1979bootstrap,10.1214/aos/1176344552,crossref,Bootstrap Methods: Another Look at the Jackknife,yes
++raasveldt2019duckdb,10.1145/3299869.3320212,crossref,DuckDB,yes (Crossref title truncated to "DuckDB"; SIGMOD 2019 demo pp. 1981-1984)

modified manuscript/sections/part02_evolution.tex +72 −1

++% ============================================================================
++% Part II — The Evolution of Computer Science
++% Written by Agent L (Academic Writer), WP44, 2026-11-03.
++% Bibliography: references_part02.bib (every key verified; see references_verification_part02.csv).
++% Corpus quantities only through the macros of tables/numbers.tex; \pending{} marks missing macros.
++% ============================================================================
++
++\section{The disciplinary structure of computer science}
++\label{sec:p2-structure}
++
++Any claim that computer science has been transformed since late 2022 presupposes a map of the discipline on which the transformation can be located. That map is not given by nature. Computer science is organised simultaneously by its objects (processors, programs, data, networks, users), by its methods (proof, construction, measurement, learning), and by the communities and venues through which results circulate; the three organisations overlap only partially, and every bibliometric classification is a compromise among them. This section describes the compromise adopted in the present study and the alternatives against which it must be read. It serves two purposes for the remainder of the manuscript: it fixes the vocabulary of \emph{areas} and \emph{categories} used in Parts~V--IX, and it makes explicit the sense in which a ``shift of research toward artificial intelligence'' is a measurable proposition rather than a slogan.
++
++\subsection{Classification schemes and what they encode}
++\label{sec:p2-schemes}
++
++Four classification systems are relevant to a corpus of the kind assembled here, and they encode the structure of the discipline in different ways.
++
++The \emph{ACM Computing Classification System} (CCS) is the only one produced by a professional society as an explicit ontology. Its 2012 revision replaced the 1998 four-level scheme with a poly-hierarchy of roughly two thousand concepts organised under thirteen top-level nodes, among them ``Theory of computation'', ``Computing methodologies'' (which houses artificial intelligence, machine learning, computer graphics and simulation side by side), ``Computer systems organization'', ``Software and its engineering'', ``Security and privacy'', ``Human-centered computing'' and ``Networks'' \citep{rous2012ccs}. The CCS is a statement of how the ACM understood the field in 2012. It is applied by authors at submission time, is used almost exclusively in ACM venues, and is therefore sparse in any corpus that spans IEEE, Springer, Elsevier, open-access publishers and preprint servers. Its value for this study is conceptual rather than operational: it provides an independent, expert-designed reference against which the forty categories of CLAUDE.md~\S4 can be positioned, and it documents a pre-transformer view in which large language models, foundation models, AI agents and AI safety do not exist as concepts at all.
++
++The \emph{arXiv subject classes} are the second system. arXiv was created in 1991 for high-energy physics and its taxonomy reflects that origin; the computer science archive (\texttt{cs}) is organised into forty subject classes largely inherited from the ACM scheme of the 1990s \citep{ginsparg2011arxiv}. Authors choose a primary class and may add cross-lists, so the classification is self-reported, multi-label, and coarse. Its distinctive property is temporal: a preprint is classified at the moment of submission, before peer review, so the arXiv class records how authors positioned their work at the time, not how a later indexer reconstructed it. For the questions of this paper that property matters: when a paper on, say, program verification with language models is submitted to \texttt{cs.PL} with a cross-list to \texttt{cs.LG}, the pair of classes is a small but genuine piece of evidence about disciplinary boundaries as practitioners perceive them.
++
++The \emph{DBLP computer science bibliography} is the third. DBLP does not classify papers at all; it classifies \emph{venues}. Begun in 1993 as a database and logic programming bibliography and extended to the whole of computer science, it records the tables of contents of conferences and journals with curated author identities \citep{ley2002dblp}. Membership in DBLP is itself a judgement that a venue belongs to computer science, and the venue stream to which a paper belongs is a strong signal of community: a paper in the proceedings of OSDI is an operating-systems paper in the sociological sense whatever its subject matter. DBLP therefore encodes the discipline as a set of communities rather than as a set of topics, and the two encodings disagree precisely where this paper expects change, namely where machine-learning methods enter venues that were not built for them.
++
++The fourth system is the family of \emph{algorithmic, citation-based classifications} that underlie modern open bibliographic indexes. OpenAlex, which succeeded the Microsoft Academic Graph after the latter was discontinued at the end of 2021 \citep{wang2020mag,priem2022openalex}, assigns to every work a ranked list of \emph{topics} from a fixed vocabulary of about 4{,}500 topics grouped into subfields, fields and domains; the topics were obtained by clustering the citation network and labelling the clusters with a language model, and are assigned to new works by a classifier over title, abstract and venue \citep{openalex2025topics}. The approach descends from the publication-level clustering methodology of \citet{waltman2012classification}, refined by the Leiden community-detection algorithm \citep{traag2019leiden}, and it inherits the known properties of that family: clusters are well connected and reproducible, but their granularity is a free parameter with no natural value \citep{sjogarde2018granularity}, and citation-based taxonomies, while more accurate than text-based or journal-based ones on average \citep{klavans2017taxonomy}, lag behind the literature by construction because a cluster must accumulate citations before it can exist. The Semantic Scholar academic graph offers a comparable but differently constructed resource \citep{kinney2023s2}. Older, journal-based classifications of the kind proposed by \citet{glanzel2003classification} remain in use in evaluative scientometrics but are too coarse, and too journal-centric, for a conference-dominated discipline.
++
++Each of these systems is a model of the discipline with its own inductive bias. The CCS encodes expert judgement frozen in 2012; arXiv classes encode author self-positioning at submission; DBLP streams encode venue communities; OpenAlex topics encode citation neighbourhoods as of a recent snapshot. None of them is ground truth, and this study treats their disagreements as data rather than as noise.
++
++\subsection{The forty-category ontology of this study}
++\label{sec:p2-ontology}
++
++The classification used throughout the manuscript is the hierarchical ontology specified in CLAUDE.md~\S4 and built in Phase~2 (Appendix~A). It comprises forty leaf categories grouped into eight areas: artificial intelligence and machine learning (nine leaves, from large language models to AI safety); perception, language, robotics and interaction (six); software, programming languages and formal methods (four); theory of computation (two); data, information and distributed systems (six); security, privacy and trust (five); hardware, architecture and emerging computing (five); and computational science and discovery (three). A small number of \emph{specialisation} edges record that one leaf refines another: large language models and multimodal AI refine foundation models; compilers refine programming languages; cloud and edge computing refine distributed computing; federated learning refines privacy-preserving computation; AI accelerators refine computer architecture. The areas are a reporting device; classification operates on leaves.
++
++The assignment rule is deliberately simple so that it can be audited. Let $\mathcal{W}$ be the set of works and $\mathcal{C}$ the set of forty leaves. For each leaf $c$ the ontology lists weighted signals of five types $k \in \{\text{topic},\ \text{subfield},\ \text{arXiv},\ \text{stream},\ \text{keyword}\}$, with signal set $S_k(c)$ and weights $\omega_{kj} \in (0,1]$ for $j \in S_k(c)$. A work $w$ matches a signal $j$ if the corresponding attribute of $w$ (an OpenAlex topic identifier, a subfield, an arXiv class, a DBLP stream, or a regular expression over title and abstract) contains it. The score of $w$ for $c$ is
++\begin{equation}
++s(w,c) \;=\; \sum_{k} \min\Bigl\{1,\ \sum_{j \in S_k(c)} \omega_{kj}\, \mathbf{1}[w \text{ matches } j]\Bigr\} \;-\; \infty \cdot \mathbf{1}[w \text{ matches an exclusion of } c],
++\label{eq:p2-score}
++\end{equation}
++so that no single signal type can contribute more than one unit, a weight of one means ``this signal alone suffices'', and an exclusion pattern vetoes the leaf outright. The work belongs to every leaf with $s(w,c) \geq 1$; its \emph{primary} leaf is $\arg\max_c s(w,c)$, with ties broken in favour of a child over its parent and then by category number. Multi-label membership is not an accident of the rule but its purpose: an instruction-tuning paper is expected to score in both large language models and natural language processing, and the share of works with more than one leaf is itself one of the quantities tracked in Part~IX.
++
++Two design choices deserve comment because they shape every downstream result. First, the ontology is not a relabelling of OpenAlex topics. Six AI categories that the citation-based topics cannot separate (large language models, foundation models, multimodal AI, generative AI, AI agents, AI safety) and seven small categories with no dedicated topic or venue (federated learning, sustainable computing, autonomous scientific discovery, privacy-preserving computation, blockchain, quantum computing, edge computing) rely on keyword patterns of weight one; the build fails if such a pattern is absent. The reason is historical and is the subject of Section~\ref{sec:p2-waves}: the citation clusters that OpenAlex labels were formed by a literature in which ``large language model'' was not yet a stable term, and a topic such as ``Topic Modeling'' in fact hosts most BERT-era and retrieval-augmented work. Second, the ontology is positioned relative to the CCS rather than derived from it. The AI area corresponds to the CCS branch \emph{Computing methodologies $\to$ Artificial intelligence / Machine learning}, but the leaves inside it are drawn from the post-2017 literature; theory, systems, networks, security and human-centred computing map onto the corresponding CCS top-level nodes almost one-to-one; computational biology, scientific computing and sustainable computing sit in CCS under \emph{Applied computing} and \emph{Social and professional topics}, which the present ontology promotes to first-class areas because the questions of this paper concern precisely the traffic between computing and the sciences it serves.
++
++The ontology's limitations are stated in Appendix~A and are not repeated here, except for one that bears on interpretation. A category defined partly by keywords is defined partly by vocabulary, and vocabulary changes faster than research practice. If the phrase ``large language model'' spreads into papers whose methods are unchanged, a keyword-driven category will register growth that is partly nominal. Section~\ref{sec:p2-claims} returns to this problem under the heading of LLM-assisted writing; Part~IV describes the embedding-based checks designed to detect it.
++
++\subsection{Where the classifications disagree}
++\label{sec:p2-disagree}
++
++The disagreements among the four systems are systematic, and they fall into three kinds.
++
++The first is \emph{coverage}. Comparisons of bibliographic databases consistently show that no source indexes the whole of a discipline and that the gaps are not random: conference proceedings, preprints, and non-English venues are the most unevenly covered \citep{visser2021comparison,martinmartin2021coverage}. OpenAlex, the backbone of this corpus, inherits the broad but uneven coverage of Microsoft Academic and Crossref; its reference lists are less complete than those of the commercial indexes for part of the period, which affects citation-based measures more than counts \citep{culbert2025openalex}. The linking phase of this study illustrates the scale of the problem at the level of record identity. The OpenAlex snapshot contains \LinkOaCsRecords{} works classified in computer science between 2018 and 2026; DBLP contributes \LinkDblpTwoZeroOneEightTwoZeroTwoSix{} records over the same years, of which \LinkDblpLinkedDistinct{} could be matched to an OpenAlex work by DOI or by title and year in the first linking pass and \LinkDblpUnlinked{} could not; the arXiv \texttt{cs} harvest contributes \LinkArxivCsRecords{} preprints, of which \LinkArxivLinkedDistinct{} were matched, overwhelmingly through the arXiv identifier that OpenAlex stores. These are linkage statistics, not findings about research, but they establish that a classification applied to one source cannot simply be transferred to another: a non-trivial fraction of the venue-defined (DBLP) universe has no citation-defined (OpenAlex) counterpart, and vice versa. Part~III describes the second linking pass and the treatment of unmatched records.
++
++The second kind of disagreement is \emph{granularity and boundary}. The arXiv class \texttt{cs.LG} is one node among forty; in OpenAlex, machine learning spreads across dozens of topics in several fields, including statistics, engineering and medicine, because the citation network does not respect the boundary of the computer-science field. Conversely, OpenAlex's field~17 (``Computer Science'') contains clusters on educational technology, media studies and physics that no computer scientist would claim, and the ontology of this study deliberately leaves most of them unmapped (Appendix~A). The practical consequence is that the apparent size of ``AI'' depends on which boundary is used: a venue-based definition (DBLP streams of NeurIPS, ICML, ICLR, AAAI and the vision and language conferences) gives a different set from a topic-based definition, which gives a different set again from an arXiv-class definition, and the three sets diverge most in the applied sciences, where machine-learning methods are used without the paper belonging to any machine-learning community. The multi-label design of the ontology, and the use of all three signal types with capped weights in equation~\eqref{eq:p2-score}, is the response to this divergence: a work that is an AI paper by every criterion scores high on all of them, while a work that is an AI paper by vocabulary alone is held to a higher evidentiary standard.
++
++The third kind is \emph{temporal}. The CCS was fixed in 2012 and the arXiv classes are older still; OpenAlex topics were generated from a citation network observed in the early 2020s and are reassigned when the snapshot is rebuilt; DBLP streams are stable but new venues appear and old ones are renamed. A classification that does not change cannot register the emergence of a field except as the growth of an existing node; a classification that is regenerated cannot be compared across snapshots without care. Neither property is a defect, but they pull in opposite directions, and a study of structural change must choose. This study freezes all classifications at a single snapshot (Part~III), so that any growth it reports is growth of a fixed category, and it treats the emergence of \emph{new} categories as a separate detection problem (Part~IV, Section~\ref{sec:p2-claims}).
++
++A final, sociological disagreement should be named explicitly because it affects what counts as a ``paper''. Computer science is unusual among the sciences in treating selective conferences as the primary venue of record, a practice that has been defended on grounds of speed and criticised on grounds of reviewing quality and the fragmentation of the literature \citep{vardi2009conferences,fortnow2009growup}. The convention has two consequences for bibliometrics. Conference papers are indexed less uniformly than journal articles, so coverage disagreements are concentrated in exactly the venues that the discipline values most; and the conference calendar imposes a strong seasonality on publication dates, so that monthly series must be deseasonalised before any break can be read off them. The preprint culture adds a third consequence. Computer scientists came to arXiv late relative to physicists and mathematicians, and a large share of computer-science preprints are never matched to a published version, either because they are not published or because the match fails \citep{lin2020preprints}. A corpus that counts preprints and published versions as separate works will over-count the preprint-heavy subfields; a corpus that links them will under-count whenever linkage fails; and the subfields where linkage fails most often are, again, the fast-moving ones.
++
++\subsection{Why the growth of a field is hard to measure}
++\label{sec:p2-growth}
++
++The questions of this paper are ultimately about rates: of publication, of citation, of movement between categories. Rates presuppose denominators, and the denominators of science are themselves contested quantities.
++
++The literature on the growth of science offers two sobering lessons. The first is that measured growth depends heavily on the database. Using cited references as a proxy for the literature that scientists actually use, \citet{bornmann2015growth} estimated the long-run growth of science at roughly three per cent per year with a doubling time of about a quarter of a century, while estimates based on the publication counts of individual databases vary widely with the database's coverage policy and its own expansion. A later analysis with segmented growth curves across several databases found that the growth rate has itself changed over time, with faster growth in recent decades, but that the location of the segments depends on the source \citep{bornmann2021growth}. For the present study the lesson is that a change in the volume of computer-science publications between 2018 and 2026 is the sum of a change in research activity and a change in indexing, and that only the first is of scientific interest. OpenAlex grew substantially during the period as it absorbed new sources and improved its matching; the indexing lag (the delay between a work's publication and its appearance in the snapshot) is longest for the most recent months, so that any series ending in 2026 slopes downward at its end for reasons that have nothing to do with research. These are among the pre-period diagnostics and sensitivity checks required by CLAUDE.md~\S6.5 and specified in Part~IV.
++
++The second lesson concerns the ``science of science'' as a field in its own right. The programme described by \citet{fortunato2018sos} and developed at book length by \citet{wang2021sos} has established robust regularities: citation distributions are heavy-tailed within every field; team size has grown steadily; novelty in the sense of atypical combinations of prior work is associated with higher impact at the top of the distribution; and the productivity of individual scientists is approximately stable over careers. Those regularities are the background against which any field-specific claim must be read. A rise in the citation inequality of AI papers after 2022, for instance, is interesting only if it exceeds the inequality that heavy-tailed citation distributions produce in every growing field; the hypotheses H1--H8 of the preregistered protocol (Part~III) are formulated as comparisons between the post-2022 period and the baseline for that reason.
++
++A further difficulty is specific to computer science and concerns what the discipline takes itself to be. \citet{denning2005science} argued that computer science is a science of information processes and not merely an engineering of artefacts, and \citet{wing2006computational} that ``computational thinking'' is a general intellectual skill rather than a professional one; both arguments were written at a moment when the discipline was defending its boundaries. The post-2022 period raises the converse question: whether the discipline's methods have diffused so widely that the boundary is no longer where the classifications place it. Papers that apply learned models to protein structure, materials, weather or law are classified as computer science by some systems and not others; a transformation that consists partly of such diffusion is partly invisible to a classification that keeps the boundary fixed. The ontology's eighth area, computational science and discovery, and the ``penumbra'' set defined in Part~III (works with a computer-science topic but without venue or arXiv support) exist to make that diffusion observable without inflating the core.
++
++\section{Three prior transformations and how they propagated}
++\label{sec:p2-waves}
++
++The post-2022 period is not the first time computer science has been reorganised around a new technology. Three earlier transformations are directly relevant because they established the publication practices, the infrastructure, and the conceptual vocabulary within which the present one unfolds: the internet and web era, which created the systems and the industrial research laboratories; the deep-learning wave of 2012--2017, which created the benchmark culture and the compute economy; and the transformer and pretraining era of 2017--2022, which created the models themselves. For each, the account below is deliberately narrow. It does not attempt a history of computing; it asks what changed in how research was done and published, and how the change propagated from the subfield in which it began to the others. The answers suggest a way of reading the post-2022 data that the empirical parts of this paper then test.
++

modified manuscript/sections/part03_data.tex +584 −6

+ Table~\ref{tab:data-sources} summarises the sources, the access route, the licence and the role of each.
+ \begin{table}[htbp]
++\centering\footnotesize
+ \caption{Information sources of the WP44 corpus (all accessed 2026-11-02/03).}
+ \label{tab:data-sources}
++\begin{tabularx}{\textwidth}{>{\raggedright\arraybackslash}p{2.5cm}>{\raggedright\arraybackslash}X>{\raggedright\arraybackslash}p{2.1cm}>{\raggedright\arraybackslash}X}
+ \toprule
+ Source & Access and snapshot & Licence & Role \\
+ \midrule
+ on either side, (iii) normalised title equality with a year difference of at most one, restricted to records
+ not yet linked on either side. The DOI step linked \LinkLinkDblpOaDoi{} records and the title step
+ \LinkLinkDblpOaTitleYear{}; the arXiv step linked none, because the extraction of arXiv URLs from the DBLP
++\texttt{ee} fields returned no candidate (\LinkDblpWithArxiv{} records with an extracted identifier). The
++\texttt{ee} fields of this dump evidently do not carry \texttt{arxiv.org/abs} URLs in the form the extractor
++expects; whether the CoRR entries are reached by the DOI step instead depends on DBLP's current conventions for
++the \texttt{ee} field, which the audit did not characterise, and the question is left to the linkage-precision
++review. The step is kept in the pipeline because other dumps may differ. In total \LinkDblpLinkedDistinct{} of the \LinkDblpTwoZeroOneEightTwoZeroTwoSix{} DBLP
+ records in the window were linked to an OpenAlex work after the first pass, leaving \LinkDblpUnlinked{}
+ unlinked. The composition of the unlinked set is informative: \LinkDblpUnlinkedByTypeArticle{} journal
+ articles, \LinkDblpUnlinkedByTypeInproceedings{} conference papers, \LinkDblpUnlinkedByTypePhdthesis{}
+ and Appendix~B, and every conclusion that depends on the title-based links is labelled as pending human review
+ until H-1 is signed.
++\section{Deduplication of preprint and published versions}
++\label{sec:data-dedup}
++
++OpenAlex creates distinct work records for an arXiv e-print and for the journal or conference version of the
++same paper whenever the two carry different DOIs, which is the normal case since arXiv registers DataCite DOIs
++for its e-prints. In a corpus where preprinting is widespread this is the dominant form of duplication, and it
++biases every count in the direction of fields that preprint most: a paper in machine learning that appears on
++arXiv and then at a conference would be counted twice, a paper in a theory journal once. OpenAlex itself links
++some preprint--publication pairs, but the links are incomplete, and prior work on discovering such links has
++shown that neither DOIs nor titles alone suffice \citep{cabanac2021preprint}. Stage S2 therefore merges
++preprint and published versions into one node per work, in the sense required by the mandate (one node per
++\emph{work}, with both dates retained).
++
++\subsection{Pairing rule}
++
++The rule is conservative by design, favouring a small number of certain merges over a larger number of
++probable ones. A \emph{preprint-like} record is one whose OpenAlex type is \texttt{preprint} or whose DOI is
++an arXiv DataCite DOI (prefix \texttt{10.48550/arxiv}); a \emph{published-like} record is any other record.
++Two records are paired when their normalised titles are identical, both titles have at least 25 characters,
++and their publication years differ by at most two. When a published record matches several preprints, the
++earliest preprint is kept as its preprint; when a preprint matches several published records, the earliest
++published record is kept; all other pairs are discarded, so that the pairing is one-to-one. The pairing is
++confined to the loaded OpenAlex records; preprints that are not computer-science candidates cannot be paired,
++which is immaterial since they would not enter the corpus. No fuzzy title similarity is used at this stage, so
++that a preprint whose title changed at publication is not merged; the consequence, a residual double count, is
++discussed below.
++
++\subsection{What is kept from each member of a pair}
++
++The published record is the surviving node. The preprint record receives the flag \texttt{duplicate\_of} with
++the identifier of the published work and is excluded with reason code E5 (Section~\ref{sec:data-eligibility}).
++The published node receives \texttt{preprint\_work\_id} and, through the arXiv linkage, the first-submission
++date of the e-print, so that for every work the \emph{first date} is defined as the earliest of its OpenAlex
++publication date, the publication date of its preprint record and the arXiv \texttt{created} date. This first
++date is the date variable of all monthly series in Part~IX; the publication date of the version of record is
++kept alongside it. The distinction matters for structural-change analysis: dating a paper by its journal issue
++would displace much of the research activity of 2023 into 2024 and 2025 and would blur any monthly series
++around the November 2022 boundary, whereas the first date places the work at the moment it entered the public
++record. Citation counts are those of the surviving published node; OpenAlex attributes to the preprint record
++the citations that cite the preprint DOI, and these are not added to the published node, a known
++under-count for works whose preprint remained the cited version. Part~IV states the resulting measurement
++assumption and Part~IX reports, for the high-priority set, the share of citations that accrue to preprint
++records.
++
++\subsection{Outcome and residual duplication}
++
++The pairing identified \PrismaExcludedEFiveDuplicate{} preprint records as duplicates of a published work,
++of which \PrismaExcludedSequentialEFiveDuplicate{} remained after the earlier exclusions had been applied.
++Set against the \PrismaEligibleByTypePreprint{} preprints that remain in the eligible corpus as works with no
++published counterpart, the figure shows that most e-prints in the candidate set were not matched to a
++published version within OpenAlex. Three explanations coexist and cannot be separated without manual review:
++many e-prints are genuinely unpublished, consistent with the publication rates reported for computer-science
++preprints \citep{lin2020preprints}; some published versions carry a modified title; and some published
++versions are not in OpenAlex or lack a computer-science topic. The first explanation is benign; the second and
++third leave a residual double count whose size is bounded in Part~IX by re-running the pairing with a title
++similarity threshold rather than exact equality and reporting the difference as a sensitivity, not as a
++correction. Because arXiv-linked works carry their e-print identifier, the sensitivity analyses can also
++remove every preprint node whose e-print has a journal DOI in the arXiv metadata, whether or not the pairing
++found the published record.
++
++\section{Eligibility criteria and exclusions}
++\label{sec:data-eligibility}
++
++Stage S3 applies the eligibility criteria of the preregistered protocol (\texttt{protocol/PROTOCOL.md}, P2) to
++every loaded record and records, for each, a set of Boolean exclusion flags with reason codes. The flags are
++computed independently so that each code can be counted on its own and also sequentially, in the order
++E1--E7, so that the PRISMA flow (Figure~\ref{fig:prisma}) sums correctly. Reporting both views is deliberate:
++the independent counts show how large each problem is in the raw data, the sequential counts show how many
++records each criterion removed given those before it.
++
++\subsection{Reason codes}
++
++\textbf{E1, outside window.} The publication date is missing, earlier than 2018-01-01 or later than
++2026-11-02. Because the first-pass filter already restricted the publication year, this code catches only
++records with a missing date or a date beyond the extraction date. Forward-dated records, that is works whose
++publication date lies in the future relative to the snapshot, are a documented feature of OpenAlex, where
++publishers register articles for forthcoming issues; the window end removes those dated after the extraction
++date, and the remaining 2026 records are discussed in Section~\ref{sec:data-dates}.
++\PrismaExcludedEOneOutsideWindow{} records are flagged.
++
++\textbf{E2, non-research document type.} The protocol lists the document types that constitute research
++output: article, conference paper, preprint, book chapter, book, dissertation, report, review and software
++paper. The exclusion operates asymmetrically. A record that is \emph{not} DBLP-linked is excluded when its
++OpenAlex type is outside the list. A record that \emph{is} DBLP-linked is kept whatever its OpenAlex type,
++unless that type belongs to a hard-exclusion list of types that are never research outputs (editorial,
++erratum, retraction notice, peer review, paratext, conference abstract, reference entry, dataset, software,
++supplementary material, library guide, letter, grant, standard). The asymmetry reflects amendment A-001 and
++is explained below. Independently of order, \PrismaExcludedETwoType{} records carry this flag. The largest
++contributors are records typed as datasets (\PrismaExcludedETwoByTypeDataset{}), followed by the
++residual type \texttt{other} (\PrismaExcludedETwoByTypeOther{}), library guides
++(\PrismaExcludedETwoByTypeLibguides{}), software (\PrismaExcludedETwoByTypeSoftware{}), conference
++abstracts (\PrismaExcludedETwoByTypeConferenceAbstract{}) and peer reviews
++(\PrismaExcludedETwoByTypePeerReview{}); paratext, reference entries, editorials, supplementary materials,
++errata, standards, book reviews, retraction notices, data papers and letters make up the remainder. The
++prominence of datasets and software among records carrying a computer-science topic is itself a finding about
++the backbone: OpenAlex indexes the DataCite registry, where repositories mint DOIs for research objects that
++are not publications, and the topic classifier labels them by their descriptions.
++
++\textbf{E3, XPAC expansion records.} OpenAlex marks as XPAC the records it added from non-Crossref sources in
++its coverage expansion; these carry sparser metadata and a higher share of non-research objects. The
++protocol excludes them, and \PrismaExcludedEThreeXpac{} records carry the flag independently. Amendment A-001
++introduced one exception: an XPAC record that is linked to a DBLP record is a computer-science publication
++whatever its provenance in OpenAlex, and it is kept with the flag \texttt{xpac\_kept\_dblp}
++(\PrismaExcludedXpacKeptDblp{} records). Sequentially, after the type criterion,
++\PrismaExcludedSequentialEThreeXpac{} records are removed under E3, far fewer than the independent count
++because most XPAC records are also non-research types.
++
++\textbf{E4, no title.} Records with a missing or empty title cannot be linked, deduplicated or embedded and are
++excluded: \PrismaExcludedEFourNoTitle{} independently, \PrismaExcludedSequentialEFourNoTitle{} sequentially.
++
++\textbf{E5, duplicate version.} The preprint member of a merged pair (Section~\ref{sec:data-dedup}):
++\PrismaExcludedEFiveDuplicate{} independently, \PrismaExcludedSequentialEFiveDuplicate{} sequentially.
++
++\textbf{E6, retracted.} Works flagged \texttt{is\_retracted} by OpenAlex, which draws on the Retraction Watch
++data now distributed through Crossref, are \emph{not} removed from the mapping corpus: they are part of the
++published record and of its volume, and their presence is itself an object of study given the record numbers
++of retractions reported for 2023 \citep{vannoorden2023retractions}. They are kept with the flag and excluded
++from every impact analysis (citation rankings, landmark selection, field-normalised scores).
++\PrismaExcludedESixRetracted{} loaded records carry the flag, of which
++\PrismaEligibleMembershipRetractedFlagged{} are in the eligible corpus.
++
++\textbf{E7, generic deposit platform.} Introduced by amendment A-002, this code excludes records whose primary
++source is a generic deposit platform (Zenodo, Figshare, the Japanese Institutional Repositories DataBase,
++LA~Referencia, Dialnet, the DOAJ-as-source entries and the OSTI OAI feed) unless the record is DBLP- or
++arXiv-linked. The number of records removed under this code is reported by the macro
++\pending{PrismaExcludedSequentialESevenGenericDeposit} once the corpus has been rebuilt under A-002.
++
++One inconsistency of numbering must be recorded. In the frozen protocol, code E7 designates the
++penumbra of works with a secondary computer-science topic only; the implementation reuses the number E7 for
++the generic-deposit exclusion of amendment A-002 and carries the penumbra as a separate flag,
++\texttt{cs\_penumbra}. The penumbra is not an exclusion in the same sense as E1--E7: it is a scope boundary,
++reported separately and used in sensitivity analyses, and is treated in Section~\ref{sec:data-core}. The
++conflict of numbering is flagged here for correction in the amendments log before the protocol is cited in
++its final form.
++
++\subsection{Amendments A-001 and A-002 and their evidence}
++
++Two eligibility decisions were taken after the protocol freeze, on the evidence of the first run of the
++corpus-definition script, and both are logged in \texttt{protocol/amendments.csv} with their date, their
++reason and their status (executing agent, pending confirmation by the principal investigator at
++checkpoint H-0). They are reported here in full because an eligibility rule changed after preregistration is
++exactly the kind of decision a reader must be able to audit \citep{nosek2018preregistration}.
++
++\emph{A-001, document types.} The protocol's type list was written from the OpenAlex documentation available
++at the time; the 2026 snapshot uses the types \texttt{conference-paper} and \texttt{software-paper}, which the
++list did not contain. The first run of the eligibility stage excluded under code E2 a very large number of records typed
++\texttt{conference-paper}, close to half of them DBLP-linked; the figures of that superseded run are recorded in
++the amendment entry and are not repeated here, since the run was not retained. Conference papers are the principal publication form of several areas of
++computer science, and their exclusion would have invalidated the composition analyses of Part~IX. The
++amendment adds the two types to the list and, more consequentially, makes DBLP linkage decisive: a
++DBLP-linked record is a computer-science research output whatever OpenAlex calls it, unless its type is one
++that can never be a research output. The same amendment keeps DBLP-linked XPAC records. The corrected run
++yields the counts reported above, in which \PrismaEligibleByTypeConferencePaper{} eligible works are
++conference papers.
++
++\emph{A-002, generic deposit platforms and date precision.} Inspection of the eligible corpus by source
++(\texttt{systematic\_review/inspect\_sources.py}) showed that a single generic repository, Zenodo, contributed
++several times more eligible records dated 2026 than dated 2025, that almost none of them carried a reference
++list, and that random samples were slide decks, datasets, software releases and project reports typed as
++articles because the depositor had chosen that type; the counts are recorded in the amendment entry. Left in the
++corpus, such records would have produced a spurious surge of 2026 output concentrated in a repository that
++does not publish research articles, and would have done so in the months where right truncation already
++makes the series fragile. The amendment excludes records whose primary source is a generic deposit platform
++unless DBLP or arXiv vouch for them, and introduces the \texttt{date\_precision} flag discussed in
++Section~\ref{sec:data-dates}. The rule is a source-level rule, not a content judgement: a genuine research
++report deposited only on Zenodo is lost, which is accepted because the alternative, keeping every deposit,
++contaminates the primary estimand of RQ1. The sensitivity analyses of Part~IX report the monthly series with
++and without the generic-deposit exclusion.
++
++The amendments illustrate a general point about preregistered bibliometrics. The protocol can freeze the
++\emph{rules} but not the data model of a database that changes between the time of writing and the time of
++extraction; when the data model changes, the honest course is an amendment with its evidence, not a silent
++adaptation.
++
++\section{The computer-science core, the penumbra and the strict core}
++\label{sec:data-core}
++
++The decision of what counts as computer science is the single most consequential modelling choice in a
++discipline-level mapping, and the protocol makes it with a three-part membership rule (decision D4) rather
++than with any one source. A record that survives the exclusions E1--E7 belongs to the \emph{core} if at
++least one of the following holds:
++
++\begin{enumerate}
++\item it is linked to a DBLP record (venue-based membership);
++\item its OpenAlex primary field is Computer Science (classifier-based membership);
++\item it carries a secondary computer-science topic \emph{and} is linked to an arXiv e-print whose primary
++  category is in the \texttt{cs} archive (author-declared membership).
++\end{enumerate}
++
++A record that survives the exclusions but satisfies none of these, which means it carries a secondary
++computer-science topic without venue or author support, belongs to the \emph{penumbra}. The three conditions
++correspond to three independent judgements about disciplinary membership: the judgement of DBLP's editors
++about venues, the judgement of OpenAlex's classifier about content, and the judgement of the authors who chose
++a primary arXiv category. Requiring agreement of any one of them with the first-pass topic filter removes the
++over-inclusion that a secondary topic alone would produce, while keeping works that any single source would
++have missed.
++
++\subsection{Counts}
++
++After the sequential exclusions, \PrismaAfterExclusions{} loaded records remain. Of these,
++\PrismaCsCoreEligible{} satisfy the membership rule and constitute the eligible mapping corpus, and
++\PrismaCsPenumbraEligibleOtherwise{} form the penumbra. The penumbra is thus a substantial fraction of the
++records that a naive ``any computer-science topic'' filter would have called computer science; its exclusion
++from the primary analyses is the main difference between the present corpus and what an OpenAlex query alone
++would return, and it is the reason why no count in this manuscript should be compared directly with the counts
++that the OpenAlex interface displays for field~17. Within the eligible corpus, the three membership
++conditions overlap: \PrismaEligibleMembershipInDblp{} works are DBLP-linked,
++\PrismaEligibleMembershipCsPrimary{} have a computer-science primary field, and
++\PrismaEligibleMembershipArxivLinked{} are linked to an arXiv e-print; \PrismaEligibleMembershipInAcl{} are
++ACL Anthology papers. The eligible corpus divides by period into \PrismaEligibleByPeriodBaseline{} works whose
++first date falls in the baseline window, \PrismaEligibleByPeriodPost{} in the post-boundary window, and a
++small residue of \PrismaEligibleByPeriodPre{} whose first date, taken from a preprint or an e-print, precedes
++2018 although the version of record falls in the window; the residue is kept in the corpus and assigned to
++the ``pre'' period, so that it enters no monthly series. Figure~\ref{fig:prisma} renders these counts as a
++PRISMA-style flow, drawn from the same JSON file that generates the macros of this section
++\citep[cf.][]{haddaway2022prismaflow}.
++
++\begin{figure}[htbp]
++\centering
++\includegraphics[width=0.9\textwidth]{fig_prisma_flow.pdf}
++\caption{PRISMA-style flow of record identification, exclusion and the final mapping corpus. Identification
++counts are per source and overlap; exclusions are sequential in the order E1--E7; the core and the penumbra
++partition the records that survive the exclusions. Source: \texttt{data/metadata/prisma\_counts.json},
++rendered by \texttt{figures/make\_figures.py}.}
++\label{fig:prisma}
++\end{figure}
++
++\subsection{The strict core}
++
++The membership rule is a disjunction, and a sceptical reader may object that a disjunction of three imperfect
++judgements inherits the errors of each. The protocol anticipates the objection with a \emph{strict core},
++defined as the conjunction of the first two conditions: works that are both DBLP-linked and labelled computer
++science by the OpenAlex classifier. The strict core contains \PrismaStrictCoreDblpAndCsPrimary{} works. Every
++headline result of Part~IX is re-estimated on the strict core, and the manuscript reports a result as robust
++to scope only when the sign and the order of magnitude agree between the eligible corpus and the strict
++core. The strict core is not a better corpus; it is a narrower one, biased toward established venues and
++against preprints, dissertations and the newer conference series that DBLP has not yet adopted, and its role
++is to bound the influence of scope decisions, not to replace them.
++
++\subsection{What the design does not do}
++
++Three limits deserve statement. First, DBLP membership is venue membership: a paper in a DBLP-indexed journal
++of bioinformatics or operations research is in the core even when its content is not computational, and the
++taxonomy of Part~IV, not the scope rule, is what distinguishes such works. Second, the rule has no language
++component; works in any language are eligible, and the share of non-English records, which is concentrated
++in dissertations and in regional journals, is reported among the quality indicators. Third, the rule is
++applied to the records loaded by the two snapshot passes; a computer-science work that OpenAlex does not
++index at all, or indexes without any computer-science topic and without a DBLP or arXiv DOI, is not observed.
++The DBLP-only table and the unmatched ACL entries bound the size of this blind spot for two sources with
++complete venue lists, and Section~\ref{sec:data-quality} reports them.
++
++\section{Dates, date precision, indexing lag and right truncation}
++\label{sec:data-dates}
++
++Monthly series are the primary temporal unit of this study, and the November 2022 boundary falls inside a
++year. Everything in the structural-change analysis of Part~IX therefore depends on the quality of dates at
++the month level, which is considerably worse than at the year level in every bibliographic database. This
++section documents three distinct problems, their diagnosis in the corpus, and the rule adopted for each.
++
++\subsection{Which date}
++
++Each eligible work carries up to three dates: the OpenAlex publication date of the surviving record, the
++publication date of its merged preprint record where one exists, and the arXiv first-submission date where the
++work is linked to an e-print. The \emph{first date} of a work is the earliest of the three
++(Section~\ref{sec:data-dedup}), and the \emph{first month} is its calendar month. The first date measures
++the entry of the work into the public record, which is the quantity relevant to questions about the pace and
++composition of research activity; the publication date of the version of record measures the pace of the
++publishing system and is kept for the analyses of venues and of preprint-to-publication lags. The two
++coincide for works without a preprint, which are the majority outside the machine-learning areas.
++
++\subsection{Date precision and the January artefact}
++
++A large share of publication dates in OpenAlex are year-only dates stored as the first of January. The
++phenomenon is visible without any model: the raw monthly series of first appearances shows a spike in every
++January that no publishing calendar could explain (Figure~\ref{fig:volume-monthly-raw}), and inspection of the
++eligible corpus by year found that between about a fifth and somewhat over a quarter of records carry a date of the form
++\emph{YYYY}-01-01, with the proportion varying by source and type; dissertations, book chapters and records
++from repositories are the most affected, journal articles with publisher-registered issue dates the least.
++Amendment A-002 introduced the flag \texttt{date\_precision}, equal to \texttt{year} when the publication
++date is the first of January and \texttt{month} otherwise. The rule is a heuristic with a known cost: a work
++genuinely published on 1 January is classed as year-precision, and since the rule is applied to the
++publication date rather than to the first date, a year-precision work with an arXiv first date is correctly
++dated by its e-print. The counts of eligible works by precision class will be reported by the macros
++\pending{PrismaEligibleDatePrecisionMonth} and \pending{PrismaEligibleDatePrecisionYear} once the corpus has
++been rebuilt under A-002.
++
++All monthly series in Part~IX use month-precision records only. Year-precision records enter the annual series,
++where their date is correct, and the composition analyses that do not depend on the month. The exclusion of
++year-precision records from monthly series is not innocuous: if the share of year-only dates differs between
++categories or between periods, the monthly composition is biased relative to the annual one. Part~IX
++therefore reports, for each category, the share of year-precision records by year, and treats a divergence
++between the monthly and the annual growth estimates as a warning rather than as two independent results.
++
++\begin{figure}[htbp]
++\centering
++\includegraphics[width=0.95\textwidth]{fig_volume_monthly_raw.pdf}
++\caption{Monthly first appearances in the eligible corpus before the precision filter. The January spikes
++reflect year-only publication dates stored as 1 January and motivate the month-precision rule. Source:
++\texttt{results/bibliometrics/volume\_monthly.csv}.}
++\label{fig:volume-monthly-raw}
++\end{figure}
++
++\subsection{Indexing lag and why \texttt{created\_date} cannot measure it}
++
++A work is observable only once the database has indexed it, and the delay between publication and indexing
++determines how incomplete the most recent months are. The research plan proposed to estimate the delay
++profile from the OpenAlex \texttt{created\_date} field, the date on which the record was created in OpenAlex,
++and to use the profile to correct the right tail of the monthly series. The diagnostic run of this estimator
++(\texttt{results/bibliometrics/indexing\_lag.csv}) refuted the proposal. For works published in 2018, the
++median difference between \texttt{created\_date} and the publication date is close to eight years, and the
++median falls by almost exactly one month for each later publication month, which is the signature of a
++constant creation date: the field records the date on which the record was created in the current OpenAlex
++data model, apparently during a re-ingestion in the second half of 2025, not the date on which the work was
++first indexed. For the great majority of works published before that re-ingestion, \texttt{created\_date}
++carries no information about indexing delay, and for works published after it the field measures the delay
++only for the records created since. A lag profile estimated from it would be an artefact of database
++maintenance. The field is therefore not used, and the plan's lag-corrected estimates are replaced by the
++approach below. The episode is recorded in detail because a reader who reproduces the estimator on a later
++snapshot may find the field meaningful again, and because it shows why the mandate's requirement to preserve
++immutable raw snapshots is not a formality: the only way to measure indexing lag honestly is to compare two
++snapshots of the same database.
++
++\subsection{Right truncation}
++
++The last months of the window are incomplete for three distinct reasons that must not be conflated: works
++published but not yet indexed (indexing lag), works indexed but not yet linked to a DBLP record because DBLP's
++own curation lags the publishers, and works whose citation counts have had little time to accumulate. The
++first two shrink the counts of the recent months; the third affects only impact indicators and is treated in
++Part~IV through fixed citation windows, which are the standard remedy for the age dependence of citation
++counts \citep{wang2013window,waltman2016review}.
++
++For the counts, the diagnosis uses the sources against one another. The DBLP dump of 2026-11-02 holds, for
++2026, a count well below its 2025 count although ten months of the year have elapsed, which shows that DBLP's
++own coverage of 2026 is incomplete; and the eligible corpus for 2026 is of the same order as that for 2025
++(\PrismaEligibleByYearTwoZeroTwoSix{} against \PrismaEligibleByYearTwoZeroTwoFive{}), which on its face
++suggests that OpenAlex is \emph{ahead} rather than behind. The two observations are reconciled by the
++composition of the 2026 records: a much lower share with a reference list, a much lower mean number of
++references, a higher share of preprints and, before amendment A-002, the Zenodo contribution described
++above. In other words, the recent months are not simply truncated; they are differently composed, with the
++fast channels (preprint servers, repositories) over-represented relative to the slow ones (journals,
++curated indexes). A correction factor estimated from totals would be wrong for every category whose channel
++mix differs from the average.
++
++The rule adopted is conservative. The ``complete'' observation window for monthly inference ends in
++June~2026; the months from July to November 2026 are reported descriptively, separately, and never enter a
++breakpoint test. Within the complete window, the structural-change models include the last months with
++their observed values and the robustness section of Part~IX re-estimates every break with the window ending
++in December 2025, so that a result that depends on the 2026 months is identified as such. The API increment
++harvested after the snapshot (Section~\ref{sec:data-sources}) adds the works registered between the snapshot
++date and the extraction date, which narrows but does not close the gap. Finally, forward-dated records,
++works carrying a 2026 publication date for an issue not yet released, are retained when their date precedes
++the extraction date because they are, by the definition of the first date, in the public record; the
++current build does not count them separately, and a count by month of the records whose publication date lies
++after the snapshot date is listed among the quality indicators to be added before Part~IX is finalised.
++
++\section{Quality indicators and known biases}
++\label{sec:data-quality}
++
++PRISMA 2020 asks for an assessment of the risk of bias in the included studies; a mapping study has no
++studies in that sense, and the protocol replaces the item with indicators of the quality of the bibliographic
++record itself, reported per year and per category in Appendix~B. This section defines the indicators,
++reports the corpus-level values that are available at the time of writing, and sets out the known biases of
++the design, each with the direction in which it is expected to push the results of Part~IX.
++
++\subsection{Metadata completeness}
++
++Four completeness indicators are computed for every work: presence of an abstract, presence of a non-empty
++reference list, presence of at least one affiliation with an institution identifier, and presence of a
++country for at least one author. In the eligible corpus, \PrismaEligibleMembershipHasAbstract{} works have
++an abstract and \PrismaEligibleMembershipHasReferences{} have at least one resolved reference, out of
++\PrismaCsCoreEligible{}. Both shares are lower than those reported for the DOI-bearing journal literature in
++comparisons of OpenAlex with the commercial indexes \citep{culbert2025openalex,delgadoquiros2024completeness},
++which is expected in a corpus that includes dissertations, book chapters and preprints, and both fall sharply
++for the most recent year (Figure~\ref{fig:types-openness}). The consequences are concrete. A work without an
++abstract cannot be embedded or classified by its content and is classified from its venue, its arXiv
++categories and its OpenAlex topics alone, with a lower confidence score; a work without references does not
++contribute out-citations to the knowledge graph, so that the citation flows of Part~VIII are estimated on the
++subset with references and are weighted accordingly. The ACL Anthology abstracts fill part of the first gap
++for one subfield; no source fills the second.
++
++\begin{figure}[htbp]
++\centering
++\includegraphics[width=0.95\textwidth]{fig_types_openness.pdf}
++\caption{Document-type composition and metadata completeness of the eligible corpus by publication year
++(2026 incomplete). Source: \texttt{results/bibliometrics/volume\_yearly.csv}.}
++\label{fig:types-openness}
++\end{figure}
++
++\subsection{Coverage overlap between sources}
++
++The overlap between sources is the study's main instrument for measuring its own coverage, because each
++source has a complete list of what it holds. Three overlaps are reported. The DBLP--OpenAlex overlap, after
++both passes, is the share of DBLP records in the window with an OpenAlex counterpart; its complement, the
++DBLP-only table, is tabulated by year and type in Appendix~B. The arXiv--OpenAlex overlap is the share of
++\texttt{cs} e-prints with an OpenAlex counterpart (\LinkArxivLinkedDistinct{} of
++\LinkArxivCreatedTwoZeroOneEightTwoZeroTwoSix{} e-prints first submitted in the window). The ACL--OpenAlex
++overlap is the share of Anthology papers linked (\LinkAclLinkedDistinctTwoZeroOneEightTwoZeroTwoSix{} of
++\LinkAclTwoZeroOneEightTwoZeroTwoSix{}). An unmatched ACL paper is a documented computer-science publication
++that the backbone either does not hold or holds under a title or identifier the linkage could not resolve;
++the residual is therefore an upper bound on the sum of backbone non-coverage and linkage failure for one
++conference-heavy subfield, and a manual review of a sample of unmatched entries, planned with checkpoint
++H-1, separates the two.
++
++Figure~\ref{fig:volume-yearly} shows the eligible works by publication year and by source membership. The
++widening of the arXiv-linked layer and the relative stability of the DBLP-linked layer are the first
++descriptive facts of the study, and they are also a warning about right truncation: in the most recent
++years, the fastest source dominates.
++
++\begin{figure}[htbp]
++\centering
++\includegraphics[width=0.95\textwidth]{fig_volume_yearly.pdf}
++\caption{Eligible works per publication year by source membership (DBLP-linked, OpenAlex computer-science
++primary field, arXiv-linked, ACL Anthology); memberships overlap. 2026 is incomplete. Source:
++\texttt{results/bibliometrics/volume\_yearly.csv}.}
++\label{fig:volume-yearly}
++\end{figure}
++
++\subsection{Known biases and their expected direction}
++
++\emph{DBLP coverage bias.} DBLP indexes established venues and adopts new conference series with a delay;
++within the core, the DBLP-linked subset over-represents long-standing journals and conferences and
++under-represents workshops, regional venues and the newest series. Because the strict core is defined
++through DBLP, every strict-core estimate inherits this bias, which runs against emerging topics and against
++the most recent years. Results that are stronger in the strict core than in the eligible corpus are therefore
++unlikely to be artefacts of over-inclusion; results that appear only in the eligible corpus may be.
++
++\emph{OpenAlex topic noise.} The topic classifier mislabels in both directions, and the membership rule
++bounds only the over-inclusion, through the penumbra. Under-inclusion is bounded by DBLP and arXiv for the
++venues and preprints they cover and is unbounded elsewhere. Within the core, the topic labels are used as
++one of several signals in the taxonomy, never alone, and the taxonomy's validation on human-reviewed samples
++(Part~IV, checkpoint H-2) is the only direct measure of how much label noise survives.
++
++\emph{XPAC records.} The expansion records excluded under E3 are not a random sample of the literature: they
++are concentrated in repositories, in non-English regions and in recent years. Their exclusion removes some
++genuine research, mostly outside the venue system, and biases the corpus toward the formal literature; the
++DBLP exception restores the part that the venue system vouches for. The sensitivity analysis that reinstates
++XPAC records of research types reports the difference.
++
++\emph{Generic-deposit platforms.} The exclusion under E7 removes, besides the deposits that motivated it, any
++research output whose only home is a generic repository; the effect is largest in 2025--2026 and in the
++areas, such as scientific computing and computational biology, where data and software deposits accompany
++papers. The direction of the bias is toward under-counting recent output in those areas, which is the
++opposite of the bias the rule corrects.
++
++\emph{Missing abstracts.} Abstract availability depends on publishers' metadata deposits and on OpenAlex's
++sources; it is lower for conference papers of some societies and for older records, and it is strongly
++correlated with the DOI registrant. Every content-based analysis is conditional on abstract availability, and
++the appendix reports the abstract share by category and year so that the reader can judge where the
++conditional population departs from the corpus.
++
++\emph{Forward-dated and year-dated records.} Both displace works along the time axis in a direction that is
++not symmetric: year-only dates pull works toward January, forward dates push them toward the future. The
++month-precision rule removes the first; the second is bounded by the window end and will be counted
++explicitly.
++
++\emph{Retractions.} Retracted works are in the corpus and out of the impact analyses. The retraction flag
++depends on the coverage of the Retraction Watch data through Crossref, which is incomplete for publishers
++that do not deposit retraction notices; some retracted works are therefore in the impact analyses
++unflagged. The direction of the bias is small but known: retraction is concentrated in a few venues and in
++a few years, and the landmark selection of Part~V checks each candidate against the current retraction
++status at the time of review.
++
++\emph{Preprint duplication.} The residual double count after the exact-title pairing inflates the areas that
++preprint most, which are also the areas expected to show the strongest growth. The sensitivity with a
++similarity threshold and the removal of preprints with a journal DOI bound the effect; a growth result for
++machine-learning areas that does not survive these two variants is reported as not robust.
++
++None of these biases is corrected by reweighting. The study reports them, bounds them where a second source
++permits, and re-estimates its results on alternative corpora (strict core; corpus with penumbra; corpus with
++XPAC research types; corpus with DBLP-only records; corpus with generic deposits) so that the reader can see
++how far each conclusion depends on each scope decision. This is the approach that the science-of-science
++literature recommends for databases whose coverage is known to be uneven \citep{fortunato2018science}, and it
++is the only one compatible with a mandate that forbids the fabrication of corrections.
++
++\section{Research-integrity procedures}
++\label{sec:data-integrity}
++
++The mandate for this working paper lists integrity rules that are unusual for a bibliometric study: no
++fabricated reference, identifier, count or result; no paper content invented from metadata; no abstract
++treated as a full text; no model-generated summary treated as evidence; no temporal association read as
++causation; no preprint described as peer-reviewed; inaccessible data recorded explicitly; and human review of
++every scientific conclusion. The rules are a response to the way the study is conducted, by a principal
++investigator directing autonomous software agents, and this section describes the procedures by which they
++are enforced. The procedures are reported here, in the data part, because most of them operate on data and
++artefacts rather than on prose.
++
++\subsection{Preregistration and the amendments log}
++
++The protocol (\texttt{protocol/PROTOCOL.md}) was frozen on 2026-11-02 before any analysis of the corpus. It
++fixes the research questions, eight directional hypotheses H1--H8 with the sign of the expected effect
++stated so that null and opposite results must be reported as such, the eligibility criteria and their codes,
++the sources and extraction dates, the data items, the quality indicators, the synthesis methods, the
++human-review checkpoints and the data-availability commitments. The preregistration is internal, in the
++repository rather than in a registry, which limits its evidential value relative to a time-stamped public
++record \citep{nosek2018preregistration}; the repository's version history and the dated amendments log are
++the available substitute, and the release of Phase~9 will include both. Every departure from the frozen
++protocol is an entry in \texttt{protocol/amendments.csv} with an identifier, a date, the phase, the change,
++the reason and the approver, which until the principal investigator signs is ``executing agent (pending
++PI)''. At the time of writing the log holds five entries: A-000 records that the mandate's acceptance
++criteria were completed by the executing agent after the pasted text was truncated; A-001 and A-002 are the
++eligibility amendments of Section~\ref{sec:data-eligibility}; A-003 and A-004 fix the budgets and the models
++of the LLM-based taxonomy validation and knowledge-graph extraction. Analyses added after the freeze are
++labelled exploratory in Part~IX.
++
++\subsection{Immutable raw snapshots and provenance}
++
++Nothing in the corpus can be traced to a result unless the raw data from which it was built can be
++re-obtained or re-verified. The OpenAlex release is a public, versioned object; the study keeps its manifest,
++the per-file content lengths and SHA-256 hashes, and the filtered subset, rather than a copy of the full
++bucket (decision D6), so that a reader with the same release and the same scripts obtains byte-identical
++inputs, and a reader with a later release can measure the difference. The DBLP dump is kept with its MD5,
++the arXiv harvest as the raw OAI-PMH pages, the ACL export with its SHA-256, and the API increment as the raw
++JSON pages with a ledger of calls and cost. Each ingestion job writes a ledger with attempts, timings, hashes,
++counts and errors, which is the job-level provenance the mandate requires. The processing chain from raw
++snapshot to PRISMA counts is a sequence of scripts with measured wall times in
++\texttt{reproducibility/RUNBOOK.md}, executed in a locked Python environment whose package versions are
++recorded; the chain was executed on a single node, deliberately, because it is network-bound and because a
++single-node execution is easier to reproduce than a distributed one.
++
++\subsection{Numbers that cannot be typed}
++
++Every count, share and estimate quoted in this manuscript is a LaTeX macro generated by
++\texttt{tables/make\_numbers.py} from the result files (\texttt{prisma\_counts.json},
++\texttt{linkage\_report.json}, the bibliometric and econometric result tables). The macros cannot be edited
++by hand, a macro whose source file is absent is undefined and stops the compilation, and a number the authors
++need but the pipeline has not produced appears in the text as a visible pending marker rather than as a
++figure. The procedure is the mechanical form of the mandate's requirement that every quantitative claim
++trace to a script, an input artefact and a result file. It has a cost in prose, since the text cannot
++anticipate a number, and a benefit in auditability, since a reader who regenerates the result files
++regenerates the manuscript's numbers.
++
++\subsection{Verified references}
++
++The bibliography of this manuscript is built under a rule that no reference may be cited unless it has been
++resolved to a bibliographic record by a single programmatic lookup against Crossref, OpenAlex or arXiv, with
++the returned title compared with the intended one and the comparison recorded in a verification table
++(\texttt{references\_verification*.csv}: key, identifier, service, returned title, match). References that
++fail the lookup are removed from the bibliography and kept in the table with a failed status, and a second
++agent re-verifies a sample in the audit phase. The rule exists because the model-based agents that draft the
++prose are known to produce plausible but non-existent references when unconstrained; it is also why
++preprints are cited as preprints, with their arXiv identifier and without a venue, and why the bibliography
++distinguishes a technical report from a peer-reviewed article in its entry type.
++
++\subsection{Agents, artefacts and the register}
++
++The study is executed by a research-director agent that spawns specialised agents with written briefs;
++each brief, the artefacts it produced and the agent that verified them are recorded in
++\texttt{agents/REGISTER.md}. The rule that governs the register is that no agent approves its own findings:
++an artefact enters the manuscript only after a different agent, the scientific critic or the reproducibility
++auditor, has checked it, and the register records the checker. Agents exchange structured artefacts (tables,
++JSON, Markdown with declared schema) rather than conversational summaries, so that what one agent passes to
++another can be inspected after the fact. The writing agents, including the one that produced this part, work
++from the source-of-truth files named in their briefs and are instructed to describe what the code does
++rather than what the plan intended; where the two differ, as with the indexing-lag estimator of
++Section~\ref{sec:data-dates}, the difference is reported.
++
++\subsection{Human-review checkpoints}
++
++Six checkpoints are defined (H-0 to H-5: plan and protocol; linkage sample; taxonomy validation sample;
++landmark list and specialist reviews; structural-change results and their interpretation; manuscript
++conclusions), each with a named artefact that the principal investigator reviews and signs
++(\texttt{reports/HUMAN\_REVIEW\_CHECKPOINTS.md}). Until a checkpoint is signed, every conclusion that
++depends on it is labelled ``pending human review'' in the text, with the same visible marker as a missing
++number. At the time of writing no checkpoint is signed. The reader should take this literally: the
++conclusions of this manuscript are, in its present version, the conclusions of a procedure, reviewed by its
++own audit agents and awaiting the judgement of its human author.
++
++\subsection{What the procedures do not guarantee}
++
++The procedures guarantee traceability, not truth. A count traced to a script is still wrong if the script is
++wrong; a verified reference is still miscited if the prose misreports what it says; a preregistered
++hypothesis can still be tested with an inappropriate model. What the procedures make possible is detection:
++every number, reference and decision has a place where an auditor can look. The remaining risks are the
++ones this part has tried to state plainly: the coverage and labelling errors of the sources, the residual
++duplication, the fragility of the recent months, and the dependence of every scope decision on the
++judgement, recorded but not infallible, of the agents that took it.
++

modified manuscript/sections/part04_methodology.tex +366 −13

+ bibliographic records and of a small number of deterministic transformations
+ applied to them. This part states those transformations with enough precision
+ that an independent analyst could recompute the results from the raw snapshots
++listed in Part III\@. The exposition follows the order of the pipeline
+ (\texttt{reproducibility/RUNBOOK.md}): record linkage, deduplication and
+ eligibility (Section~\ref{sec:m-linkage}), multi-label classification
+ (Section~\ref{sec:m-classification}), bibliometric indicators
+ \LinkLinkDblpOaTitleYear{} by title and year, for \LinkDblpLinkedDistinct{}
+ distinct DBLP keys linked and \LinkDblpUnlinked{} unlinked. No DBLP record was
+ linked through the arXiv step (\LinkDblpWithArxiv{} DBLP keys with a parsable
++arXiv identifier), a result consistent with DBLP recording arXiv e-prints
++through their DataCite DOIs (prefix 10.48550), which the DOI step captures,
++rather than through \texttt{arxiv.org/abs} URLs; the cause is to be confirmed
++in the audit of Part III\@. The arXiv--OpenAlex cascade links \LinkArxivLinkedDistinct{} of the
+ \LinkArxivCreatedTwoZeroOneEightTwoZeroTwoSix{} arXiv \texttt{cs} records created
+ in window, almost all of them (\LinkLinkArxivOaArxivId{}) by identifier. The
+ \LinkDblpUnlinkedWithDoiInSlim{} unlinked DBLP records whose DOI does exist in the
+ \label{sec:m-truncation}
+ Each series is a vector of monthly counts $N_t$ on the month-precision
++subset, from February 2018 (the default start of the driver script, which
++drops one month and is immaterial) to the last complete month
++$t_{\mathrm{L}}$, by default June 2026, with sensitivity runs at March and
++August 2026. Category series $N_{k,t}$ use the multi-label count restricted to
+−direction (year-precision works with a genuine January date are not
+−recoverable). Category series $N_{k,t}$ use the multi-label count restricted to
+ month-precision works, and a series is analysed only if it has at least 48
+ non-zero months and at least 500 works in total; smaller series yield
+ unstable fits and are reported descriptively. All methods operate on
+ with the break positions recovered by back-tracking. The minimum segment
+ length is $h = \max\{8, \lfloor 0.15\, n \rfloor\}$ months, a trimming of 15 per
+ cent that is the usual choice in this literature; it forbids breaks in the
++first and last $h$ months, which for the default window of 101 months
++($h = 15$) means that no break can be placed after April 2025, and it also
++forbids two breaks closer than $h$ months. The number of breaks, up to three, is selected by two
+ criteria computed on the global minima $\mathrm{SSR}_m = C_m(n)$,
+ \begin{align}
+   \mathrm{BIC}(m) &= n \log\bigl(\mathrm{SSR}_m / n\bigr) + p_m \log n, \\
+ the sensitivity of the reference-date ITS and of the Bai--Perron dates to the
+ truncation month $t_{\mathrm{L}}$ is also tabulated.
++\section{Emerging-topic detection}
++\label{sec:m-emerging}
++
++The framework of RQ10 asks whether signals observable at a month $T$ predict
++which topics will have grown unusually by $T + h$. Its scientific content lies
++less in the models, which are deliberately simple, than in the discipline of
++the evaluation: features are computed from data dated at or before $T$,
++labels from data strictly after $T$, training cutoffs end before test cutoffs
++begin, and the test labels are observed in a period the training never saw.
++The implementation is \texttt{experiments/emerging\_detection.py}.
++
++\subsection{Units and panel}
++
++The units are OpenAlex topics (the \texttt{rank 0} topic of each work) with at
++least fifty eligible works over the whole window. Topics are used rather than
++the forty ontology categories because the categories are too few and too
++broad to emerge, and rather than the semantic clusters of
++Section~\ref{sec:m-semantic} because a cluster defined on the full corpus is
++not an out-of-time object: it would be known to exist only because of works
++after $T$. The plan lists semantic clusters re-estimated at each cutoff and
++keyword $n$-grams as additional unit types; neither is implemented in the
++current pipeline, and the manuscript reports topic-level results only. Topics
++have their own limitation in this role: the OpenAlex topic model is itself
++fixed in 2026 and assigns 2019 works to topics defined with knowledge of
++2019--2026 literature, so a topic that ``exists'' in 2019 with a handful of
++works may be a 2024 construct projected backward. The label definition below
++partly neutralises this, because it conditions on the topic having at least
++twenty works in the year before $T$, but the caveat applies to any
++retrospective evaluation with a fixed vocabulary and is stated as the first
++limitation of the framework.
++
++The panel consists of two $K \times M$ matrices over topics $k$ and months
++$t$ from January 2018 to the last month (October 2026 by default): the volume
++$n_{k,t}$, the number of month-precision eligible works of topic $k$ with
++first month $t$, and the citation inflow $c_{k,t}$, the number of internal
++citation edges whose cited work belongs to topic $k$ and whose citing work has
++first month $t$. Citation inflow is dated by the citing work's first month,
++not by the OpenAlex calendar-year counts, which is what makes it usable at
++monthly resolution and out of time.
++
++\subsection{Features at a cutoff}
++
++Write $V_k(a, b] = \sum_{t = a+1}^{b} n_{k,t}$ for the volume of topic $k$ in
++the months $a < t \le b$ (and likewise $C_k$ for citations). At cutoff $T$ the
++feature vector of topic $k$ is
++\begin{align}
++  g^{12}_{k,T} &= \log\bigl(1 + V_k(T{-}12, T]\bigr) - \log\bigl(1 + V_k(T{-}24, T{-}12]\bigr), \\
++  \mathrm{acc}_{k,T} &= g^{12}_{k,T} - g^{12}_{k,T-12}, \\
++  z_{k,T} &= \frac{\tfrac13 V_k(T{-}3, T] - \bar n_{k,[T-27,\,T-3]}}{s_{k,[T-27,\,T-3]} + 10^{-6}}, \\
++  \mathrm{cit}_{k,T} &= \log\bigl(1 + C_k(T{-}12, T]\bigr) - \log\bigl(1 + C_k(T{-}24, T{-}12]\bigr), \\
++  \mathrm{share}_{k,T} &= V_k(T{-}12, T] \Big/ \sum_{k'} V_{k'}(T{-}12, T], \\
++  \mathrm{age}_{k,T} &= T - \min\{ t : \textstyle\sum_{u \le t} n_{k,u} \ge 5 \},
++\end{align}
++where $\bar n$ and $s$ are the mean and standard deviation of the monthly
++volumes over the 25 months from $T-27$ to $T-3$. The first is twelve-month
++log growth, the second its change over a year (acceleration), the third a
++$z$-score of the last quarter's monthly volume against the trailing two years
++(the ``abnormal growth'' signal, which also serves as the baseline detector),
++the fourth citation acceleration, the fifth a size control, the sixth the
++topic's age in months since it reached five works. Every window ends at $T$;
++the one-month overlap between the $z$-score's trailing window and the
++quarter it is compared with is immaterial. The log-ratio form of growth with
++the $+1$ shift keeps the features finite for topics that were empty in the
++earlier window. Of the seven candidate signals of the mandate, the
++implemented set covers abnormal growth, citation acceleration and (through the
++panel of Section~\ref{sec:m-novelty}, which the script can merge but does not
++by default) semantic novelty; interdisciplinary connectivity is described in
++the script's documentation as a cross-area share but is not computed, and
++methodological adoption, benchmark proliferation and open-source activity are
++not implemented. These absences are recorded as scope limits of the current
++framework; the evaluation design does not depend on which features are
++present.
++
++\subsection{Label at horizon \texorpdfstring{$h$}{h}}
++
++For a horizon $h \in \{12, 24\}$ months, the future growth of topic $k$ after
++cutoff $T$ is
++\begin{equation}
++  g^{\mathrm{fut}}_{k,T,h} = \log\bigl(1 + V_k(T, T{+}h]\bigr) - \log\bigl(1 + V_k(T{-}h, T]\bigr),
++\end{equation}
++and the topic is \emph{eligible} at $T$ if $V_k(T{-}12, T] \ge 20$. The label is
++\begin{equation}
++  \ell_{k,T,h} \;=\; \mathbf{1}\Bigl[\, g^{\mathrm{fut}}_{k,T,h} \ge q_{0.9}\bigl(\{ g^{\mathrm{fut}}_{k',T,h} : k' \text{ eligible at } T \}\bigr)
++     \;\wedge\; V_k(T, T{+}h] \ge 2 \max\{ V_k(T{-}h, T],\, 1 \} \;\wedge\; k \text{ eligible} \Bigr],
++  \label{eq:label}
++\end{equation}
++the topic is in the top decile of future growth among eligible topics at that
++cutoff and at least doubles its volume. The decile is defined within cutoff,
++so that the positive rate is roughly ten per cent at every $T$ regardless of
++the overall growth of the corpus; the doubling condition removes topics that
++reach the top decile by growing from a small base in a quiet period. The
++protocol phrases the label as ``top-decile growth and citation share''; the
++implemented label uses volume growth only, and citation share is a feature
++rather than part of the outcome. This is a deliberate simplification---a
++citation-based outcome at a twelve-month horizon is dominated by the
++right-truncation of citations---and is recorded as a deviation. The label
++uses only $n_{k,t}$, so a topic can be ``emerging'' in this sense while its
++citation impact is unremarkable; Part IX reports the citation profile of the
++positives separately.
++
++\subsection{Cutoffs, models and metrics}
++
++Training cutoffs are every third month from December 2019 to December 2021;
++test cutoffs are every third month from January 2023 to October 2024 for
++which $T + h$ lies within the panel. With $h = 24$ the last training label
++uses data to December 2023, which is after the reference date but more than a
++year before the first test label window closes, and no test cutoff precedes
++2023; the twelve months of 2022 are used by neither set as a cutoff, which
++is the temporal gap that separates training from test in the sense of
++\citet{bergmeir2012crossval}. The plan proposed training cutoffs through 2022;
++the implementation stops at December 2021 so that the training labels do not
++extend into the test period at $h = 24$, a stricter separation than the plan.
++Rows are the eligible (topic, cutoff) pairs; features are the six quantities
++above with non-finite values replaced by zero.
++
++Three predictors are compared: the $z$-score baseline, which ranks topics by
++$z_{k,T}$ alone and involves no fitting; an $\ell_2$-regularised logistic
++regression (regularisation strength $C = 1$, on unstandardised features, so
++the penalty is not scale-invariant and acts most on the small-range features);
++and a gradient boosting classifier \citep{friedman2001gbm} with 300 trees of
++depth three and learning rate 0.05, seed 44, as implemented in scikit-learn
++\citep{pedregosa2011sklearn}. The metrics on the pooled test rows are the area
++under the ROC curve \citep{hanley1982auc}, which is the probability that a
++randomly chosen positive is ranked above a randomly chosen negative, and
++precision at $k \in \{20, 50\}$, computed within each test cutoff as the share
++of positives among the $k$ highest-ranked topics and averaged over cutoffs; the
++AUC is also reported per cutoff to show its stability. Hypothesis H8 sets the
++bar at an AUC above 0.70 and precision at 50 above 0.40 against the baseline.
++Because the positives are defined by a within-cutoff decile, precision at 50
++has a chance level of about ten per cent, and a precision at 50 of 0.40
++means that twenty of the fifty flagged topics were among the positives of
++that cutoff.
++
++\subsection{Lead time and the preregistered known-emergence set}
++
++The protocol fixes before estimation a list of twelve developments that
++anyone familiar with the period would call emergent (large language models,
++diffusion models, neural radiance fields, LLM agents, retrieval-augmented
++generation, state-space sequence models, vision--language models, mechanistic
++interpretability, speculative decoding, mixture of experts at scale,
++test-time compute and reasoning models, AI for code generation) and predicts
++that the detector flags each at least six months before its peak growth
++month. The evaluation requires a mapping from each development to one or
++more OpenAlex topics, and this mapping is the weak point of the design: it
++must be made by hand, after the taxonomy work, and a development that has no
++topic of its own (because the OpenAlex topic model folds it into a larger
++one) cannot be detected by a topic-level detector whatever its merits. The
++script reserves a table for this mapping and it is empty in the current
++version; the lead-time statistic is therefore
++\pending{known-emergence lead times, experiments/known\_set.csv not yet populated},
++and the mapping, when made, will be listed in the appendix with the reason
++for each choice. Lead time itself is defined, for a development mapped to
++topic $k$, as the number of months between the first test cutoff at which
++$k$ ranks in the top 50 and the month of maximum $g^{12}_{k,t}$ over the
++window; a development whose peak growth precedes the first test cutoff has
++undefined lead time and is reported as such rather than dropped.
++
++\subsection{Look-ahead safeguards and what they do not cover}
++
++The safeguards are structural: features are functions of panel columns up to
++$T$ (enforced by index arithmetic, not by convention), labels of columns after
++$T$, cutoffs are separated by a gap, and the panel is built once from the
++corpus so that the same data serve all cutoffs. What they do not cover is
++information that entered the panel through steps performed in 2026 with full
++knowledge of the period: the topic vocabulary, the eligibility rules, the
++linkage, and the month-precision filter. A detector evaluated this way answers
++``given the 2026 topic model, would these signals have ranked the eventual
++winners early?'', which is a legitimate question about the signals and a
++weaker one about the deployability of the system. The semantic-cluster
++extension that the plan describes, with clusters re-estimated at each cutoff
++from embeddings of works dated before it, is the design that would address
++this, and it is left as the stated next step of the framework.
++
++\section{Knowledge-graph extraction}
++\label{sec:m-kg}
++
++\subsection{Structural and extracted layers}
++
++The knowledge graph has two layers of very different epistemic status. The
++structural layer---works, authors, institutions, countries, sources, OpenAlex
++topics, arXiv categories, taxonomy categories, and the citation and
++classification edges among them---is derived from the source databases by the
++deterministic rules of Sections~\ref{sec:m-linkage}--\ref{sec:m-networks}; its
++errors are the errors of the sources and of the classifier, which are
++quantified. The extracted layer---methods, models, datasets, benchmarks,
++problems, metrics and claims, with typed edges to the works that introduce,
++use, evaluate on, extend, compare against or address them---is produced by a
++language model from abstracts, for the high-priority set only, and its
++errors are of a different kind: omissions, hallucinated entities, wrong
++roles, and claims paraphrased beyond what the abstract states. The mandate
++requires that every inferred relationship carry provenance, confidence and a
++verification status, and the extraction is designed so that these three
++fields are not decorative.
++
++\subsection{High-priority and landmark sets}
++
++The high-priority set is selected by \texttt{literature\_synthesis/select\_landmarks.py}
++from eligible, non-retracted works with a primary category: within each
++(primary category, publication year) cell the works are ranked by
++$\mathrm{NCS}^{(36)}$ (for 2025 and 2026, for which the window is empty, by
++$c^{(12)}$ divided by its year mean), and the top $\max\{25,\ \mathrm{round}(10{,}000\, n_{\mathrm{cell}} / n)\}$
++are taken, so that cells are represented in proportion to their size with a
++floor that keeps small categories and recent years present. The landmark set
++takes the top $\max\{15, \lfloor 1000/40 \rfloor\} = 25$ works of each category by
++the same score, adds the 200 works with the highest $c^{(36)}$ overall, and
++removes duplicates; it is flagged for human review (checkpoint H-3) in its
++entirety. Both selections are citation-based and inherit the biases of
++citation counts: they favour the categories and years whose works are cited
++early, and they cannot contain a work whose influence has not yet appeared in
++citations, which for 2025--2026 means that the ``high-priority'' recent works
++are the early-cited ones, not necessarily the important ones.
++
++\subsection{Schema, provenance and machine verification}
++
++For each work in the high-priority set with an abstract, the extractor
++(\texttt{knowledge\_graph/03\_extract\_entities.py}) submits the title, year and
++abstract (truncated to 3,000 characters), in batches of five works, to
++\texttt{claude-sonnet-5-5} with a JSON schema enforced by the API's structured
++output mode. The schema requires, per work, a contribution type from
++\{theory, method, empirical, benchmark, engineering, survey, other\}; a list of
++entities each with a name, a kind in \{method, model, dataset, benchmark,
++problem, metric\}, a role in \{introduced, used, evaluated\_on, extended,
++compared\_against, addressed\}, a supporting \emph{span} and a confidence; and
++a list of claims each with a text, a kind in \{result, improvement,
++negative\_result, reproduction, contradiction\}, a span and a confidence. The
++system prompt instructs the model to quote a verbatim span of at most 25 words
++from the abstract for every entity and claim, to use canonical names, and never
++to infer anything not stated. The role vocabulary maps onto the edge types of
++the mandate (\textit{introduces method}, \textit{uses dataset},
++\textit{evaluates}, \textit{extends}, \textit{addresses research problem});
++\textit{contradicts} and \textit{reproduces} are claim kinds rather than
++entity roles, because a contradiction or a reproduction is a relation between
++a work and a prior finding that an abstract rarely names as an entity.
++
++Provenance is the pair (work identifier, span). Machine verification is the
++check that the span occurs verbatim, after lower-casing and trimming, in the
++abstract the model was given:
++\begin{equation}
++  \mathrm{status}(e) \;=\;
++  \begin{cases}
++    \text{machine-verified} & \text{if } \mathrm{lower}(\mathrm{span}(e)) \subseteq \mathrm{lower}(\mathrm{abs}(w)),\\
++    \text{unverified}       & \text{otherwise},
++  \end{cases}
++\end{equation}
++with \emph{human-verified} reserved for edges the principal investigator has
++reviewed. The check is weak in a precise sense: it establishes that the
++evidence quoted for an edge exists, not that it supports the edge. A model
++that quotes a genuine sentence and attaches the wrong role to it produces a
++machine-verified wrong edge. The confidence field is the model's own stated
++probability that the extraction is correct and is not calibrated by this
++study; it is stored so that downstream users can threshold it and so that its
++calibration can be estimated on the human-verified subsample. Every record
++carries \texttt{evidence\_type = abstract}, and the manuscript treats
++abstract-derived edges as what they are: evidence that an abstract asserts a
++relation, which is not a full-text review and is never cited as establishing
++a scientific finding (CLAUDE.md §1). Extractions are appended to a
++resumable file, requests that the model refuses are skipped and counted, and
++the cost is tracked against the budget of amendment A-004. What is and is not
++evidence can therefore be stated plainly: citation edges are evidence of
++citation; classification edges are evidence of rule firing with measured
++agreement; extracted edges are evidence that a language model, reading an
++abstract, asserted a relation and quoted a sentence that exists.
++
++\section{Uncertainty, provenance and reproducibility}
++\label{sec:m-uncertainty}
++
++\subsection{What is uncertain and how it is quantified}
++
++The descriptive quantities of Sections~\ref{sec:m-bibliometrics}
++and~\ref{sec:m-networks} are computed on the whole eligible corpus and carry no
++sampling error; their uncertainty is measurement error, which enters through
++linkage (Section~\ref{sec:m-linkprecision}), classification
++(Section~\ref{sec:m-validation}), reference resolution and the completeness
++of affiliations and abstracts, each of which is estimated and reported as a
++rate rather than propagated into intervals. Propagating a classification
++precision of, say, 0.85 into an interval for a category share would require a
++model of how errors are distributed over time, and the honest statement is
++that a category's share is known up to the classifier's error, which is
++reported by category and, where the validation sample permits, by period.
++
++Three families of quantities do carry explicit intervals. The ITS
++coefficients carry HAC standard errors and the placebo rank of
++Section~\ref{sec:m-placebo}; the Bai--Perron break dates carry no interval
++in the current implementation and are reported with the agreement of the two
++criteria and the sensitivity to truncation. The detector metrics carry
++per-cutoff values, whose dispersion across cutoffs is the relevant measure of
++stability, since the rows within a cutoff are not independent. And the
++cluster count and the category similarity matrices carry the split-half ARI\@.
++Where the manuscript reports a bootstrap interval for a corpus statistic (the
++Gini coefficient by year, the cross-area shares), it is the percentile
++interval of the non-parametric bootstrap \citep{efron1979bootstrap} over
++works, with 1,000 replications and seed 44, computed by the table-making
++script at reporting time; the protocol's commitment to ``bootstrap or HAC
++intervals'' is met by these two devices, and a reported figure without an
++interval is a population count for which none is appropriate. The
++indicators of Section~\ref{sec:m-bibliometrics} that depend on OpenAlex's
++nightly recomputation (FWCI, total citation counts) are snapshot quantities
++and are dated.
++
++\subsection{Seeds and determinism}
++
++Every stochastic step is seeded with the constant 44: the validation and
++linkage samples (hash ordering salted with the seed), the atlas sample and the
++two UMAP fits, the split-half permutations, the gradient boosting classifier,
++and the bootstrap. The regular-expression classifier, the SQL stages and the
++structural-change toolkit are deterministic. Two steps are deterministic only
++up to the platform: embedding on Apple-silicon GPUs with float16 storage
++produces vectors that differ at the fourth decimal from a CPU run, and UMAP's
++parallel optimisation is reproducible only with a fixed seed and a fixed
++thread count, which the runbook records. The LLM-based steps (two taxonomy
++raters, one extractor) are not deterministic even at zero temperature across
++model versions; their outputs are archived in full (the JSONL rating and
++extraction files, with the model identifier and the usage of every call) so
++that every downstream number can be recomputed from the archived outputs
++without re-querying the model, and a re-run with a later model version is a
++new experiment, not a replication.
++
++\subsection{Ledgers, hashes and the chain of custody}
++
++The raw inputs are pinned as follows. Each OpenAlex snapshot file downloaded
++from the public bucket is hashed with SHA-256 on arrival and the hash,
++record count, computer-science record count and malformed-record count are
++written to a SQLite ledger keyed by the file's URL and manifest entry; a file
++whose hash or count does not match the manifest is re-downloaded. The DBLP
++XML dump is identified by the MD5 published with it (verified before parsing)
++and its size; the ACL Anthology export by the SHA-256 of the file; the arXiv
++OAI-PMH harvest by the raw XML pages, kept verbatim, from which the Parquet
++table is rebuilt. Every derived table is written by a script whose
++invocation, inputs, outputs and measured wall time are listed in
++\texttt{reproducibility/RUNBOOK.md}, and the Python environment is locked
++(\texttt{reproducibility/environment\_M3U96b.txt}). Numbers enter the
++manuscript only through \texttt{manuscript/tables/numbers.tex}, generated by
++\texttt{tables/make\_numbers.py} from the result files, so that a number in
++the text can be traced to a macro, the macro to a result file, the result file
++to a script and the script to a ledger entry. The chain has one weak link,
++stated here: the result files on the compute node are not themselves hashed
++into the repository, so a reader verifying the chain must either re-run the
++pipeline or trust the node's copy; the release archive of Phase 9 will add
++their hashes.
++
++\subsection{Human checkpoints and the status of conclusions}
++
++Five checkpoints (H-1 to H-5, \texttt{reports/HUMAN\_REVIEW\_CHECKPOINTS.md})
++require the principal investigator's review of the linkage sample, the
++taxonomy validation sample, the landmark list, the structural-change results
++and the conclusions. Until a checkpoint is signed, the manuscript marks the
++conclusions that depend on it as pending human review, and the numbers that
++depend on it are rendered by the \texttt{\textbackslash pending} macro rather
++than typed. This is the mechanism by which the mandate's requirement that no
++agent approve its own findings is made visible in the text rather than
++asserted in a preface; it also means that a version of this manuscript
++compiled before the checkpoints are signed contains visibly incomplete
++statements, which is the intended behaviour.
++

modified protocol/amendments.csv +1 −0

				`@@ -4,3 +4,4 @@ A-001,2026-11-03,1,"Eligibility type list corrected: OpenAlex (2026) types confe`
4	A-002,2026-11-03,1,"Records whose primary source is a generic deposit platform (Zenodo, Figshare, IRDB, LA Referencia, Dialnet, DOAJ-as-source, OSTI) are excluded unless DBLP- or arXiv-linked (flag e7_generic_deposit); date_precision flag added (publication_date = YYYY-01-01 treated as year precision; monthly series use month-precision records only)","Zenodo alone contributes 138,533 eligible records in 2026 (20,141 in 2025; 1.3% with references) — deposits of slides, datasets and reports typed as articles; 18–28% of records carry a year-only date defaulting to 1 January",executing agent (pending PI)	4	A-002,2026-11-03,1,"Records whose primary source is a generic deposit platform (Zenodo, Figshare, IRDB, LA Referencia, Dialnet, DOAJ-as-source, OSTI) are excluded unless DBLP- or arXiv-linked (flag e7_generic_deposit); date_precision flag added (publication_date = YYYY-01-01 treated as year precision; monthly series use month-precision records only)","Zenodo alone contributes 138,533 eligible records in 2026 (20,141 in 2025; 1.3% with references) — deposits of slides, datasets and reports typed as articles; 18–28% of records carry a year-only date defaulting to 1 January",executing agent (pending PI)
5	A-003,2026-11-03,2,"LLM rating budget for taxonomy validation fixed at 25 USD (two raters: claude-haiku-5-5 and claude-sonnet-5-5, structured outputs); knowledge-graph extraction budget fixed separately when designed","mandate §7 requires agent-based validation with human checkpoints; the PI is the third rater on 200 works",executing agent (pending PI)	5	A-003,2026-11-03,2,"LLM rating budget for taxonomy validation fixed at 25 USD (two raters: claude-haiku-5-5 and claude-sonnet-5-5, structured outputs); knowledge-graph extraction budget fixed separately when designed","mandate §7 requires agent-based validation with human checkpoints; the PI is the third rater on 200 works",executing agent (pending PI)
6	A-004,2026-11-03,3,"Knowledge-graph extraction from abstracts of the high-priority set with claude-sonnet-5-5 (structured outputs, provenance spans, machine verification of spans); budget 150 USD","mandate §6.4 requires provenance, confidence and verification status on every inferred relationship; abstract-level evidence is labelled as such",executing agent (pending PI)	6	A-004,2026-11-03,3,"Knowledge-graph extraction from abstracts of the high-priority set with claude-sonnet-5-5 (structured outputs, provenance spans, machine verification of spans); budget 150 USD","mandate §6.4 requires provenance, confidence and verification status on every inferred relationship; abstract-level evidence is labelled as such",executing agent (pending PI)
		7	+A-005,2026-11-03,1,"Exclusion code E7 redefined as the generic-deposit exclusion (A-002); the CS penumbra is a membership flag, not an exclusion code","Part IV writer found the protocol and code labels inconsistent",executing agent (pending PI)

added reports/DISCREPANCIES_PLAN_VS_CODE.md +15 −0

++# Plan/protocol vs code — discrepancies reported by the Part IV writer (2026-11-03) and their disposition
++
++| # | Discrepancy | Disposition (Agent A) |
++|---|---|---|
++| 1 | ontology.yaml note says "max over matched topics"; classify.py sums weights then caps at 1 per signal | code is the rule; YAML note to be corrected (taxonomy/src) |
++| 2 | VALIDATION.md promises Krippendorff α (MASI); validate_sample.py computes mean Jaccard | add α (MASI) to validate_sample.py --metrics |
++| 3 | Protocol E7 = penumbra; code e7 = generic-deposit exclusion | rename in protocol: E7 generic deposit (A-002), penumbra = separate flag, amendment A-005 |
++| 4 | H5 says Gini on 24-month citations; code uses c36; c12 = pub year + next calendar year | report both c12 and c36 Gini; define windows as calendar-year windows explicitly (Part IV already does) |
++| 5 | Placebo breaks run over baseline AND post months | add `--placebo-baseline-only` option and report both |
++| 6 | Right truncation by last-complete-month cut + sensitivity, not lag correction; no donut windows / binary segmentation / sup-F | document; add donut-window option to ITS (exclude ±3 months); sup-F not planned (DP + BIC/LWZ kept) |
++| 7 | ITS event = first full month after each date | document (already in text) |
++| 8 | Semantic centroids by calendar-year periods; split-half ARI on one UMAP fit | document as implemented; optional bootstrap later |
++| 9 | Emerging detector: topics only, volume-only label, fewer features, cutoffs to 2021-12, unstandardised logistic | add feature standardisation + novelty/cross-area merge when available; label with citation-share variant as sensitivity; cutoffs extended to 2022-09 (labels observable by 2024-09 for h=24) |
++| 10 | No igraph author/institution networks; CAGR, bootstrap CIs, diversity indices not scripted | add bibliometrics/04_diversity_cagr.py (CAGR, Rao–Stirling, Leinster–Cobbold, bootstrap CIs) and institution/country co-authorship graph metrics |
++| 11 | DBLP arXiv-step linked 0 records (DBLP now uses 10.48550 DOIs) | documented in Part III; DOI step already captures them |

added reports/audit/code_issues.csv +68 −0

++file,line,severity,type,description,suggested_fix
++systematic_review/define_corpus.py,"52,55,65-66",blocking,null-handling,"NULL `type` (e2_type) or NULL `source_name` for a non-DBLP, non-arXiv record (e7_generic_deposit) makes the flag NULL and therefore `eligible` NULL: the work is silently dropped by every downstream `WHERE eligible` AND from every PRISMA exclusion bucket; sequential counts no longer sum to the identified total (verified on synthetic DuckDB data: 2 of 6 records vanish).","Use coalesce(c.type,'') and coalesce(c.source_name,'') in the flag expressions; assert count(*) WHERE eligible IS NULL = 0; add a PRISMA identity test identified = sum(sequential exclusions) + after_exclusions."
++econometrics/run_structural_change.py,23-28,blocking,logic,"`series_categories` indexes each category by `first_month` without aggregating; category_counts_monthly.csv has one row per (first_month, period, category) and the boundary month 2022-11 carries both baseline and post rows -> duplicate index -> `reindex` raises ValueError (verified with pandas 3.0). All category-level ITS/Bai-Perron runs (H3) fail.",Use g.groupby('first_month')[col].sum() before reindexing.
++bibliometrics/03_citation_impact.py,26-29,major,null-handling,"`JOIN ... USING (year, type, sf)` is an inner equality join: every work with NULL primary_subfield_id (no OpenAlex topic, typical of pass-2 DBLP-linked works) is dropped from work_citation_windows (verified: 1 of 3 synthetic rows kept).","LEFT JOIN with `IS NOT DISTINCT FROM` or coalesce(sf,-1) on both sides; assert row count unchanged."
++bibliometrics/03_citation_impact.py,"4-5,20-22,35",major,definition / protocol mismatch,"c12 = citations in calendar years y and y+1 (13-24 months depending on publication month), c36 = y..y+2 (25-36 months); names suggest 12/36 months. Protocol H5 specifies the Gini of 24-month citations; the script computes the Gini on c36.","Rename to c_y1/c_y2 (calendar-year windows), use the y..y+1 window for H5, state the publication-month dependence (December papers get ~13 months), and add a month-precision sensitivity (restrict to works published Jan-Jun)."
++bibliometrics/03_citation_impact.py,"19,32-37",major,right-truncation,"Window statistics (mean, median, Gini, uncited share) are produced for every year although c36 needs y+2 <= last complete year and c12 needs y+1; 2024-2026 (c36) and 2025-2026 (c12) are truncated and not flagged.",Add `window_complete` per year/window and NULL the truncated statistics; derive the last complete citation year from the snapshot date and the indexing-lag profile.
++bibliometrics/03_citation_impact.py,19,major,protocol mismatch,Impact tables include retracted works (`eligible` does not exclude e6_retracted); protocol P2 E6 says retracted works are excluded from impact analyses.,Add `AND NOT e6_retracted` to the E CTE (and to top_cited_by_year).
++bibliometrics/03_citation_impact.py,20-23,major,dedup / undercount,"Citations received by the merged preprint version (record flagged e5_duplicate) are ignored: only the published record's counts_by_year is summed, so merged works (plan S2 'one node per work') undercount early citations - CS preprints are the version most often cited.",Aggregate counts_by_year over the published id and its preprint_work_id (deduplicating citing works through oa_references where both versions are cited).
++knowledge_graph/01_citation_graph.py,19-26,major,dedup / undercount,"Internal edges require both ends eligible; references to the merged preprint (e5_duplicate, not eligible) become external edges, so the merged node loses inbound citations; category flows (02) and the emerging-detection citation inflow inherit the undercount.",Remap r.ref_id through corpus_works.duplicate_of before the joins; report the number of remapped edges.
++systematic_review/define_corpus.py,38-39,major,null-handling / dedup,"`pub` CTE uses `NOT is_arxiv_doi`, which is NULL when doi is NULL: published records without a DOI (many NeurIPS/ICLR/ACL-type venue records in OpenAlex) can never be matched to their preprint, so the most AI-central population stays double counted.","Use coalesce(is_arxiv_doi, FALSE); report the number of published records without DOI."
++systematic_review/define_corpus.py,"58,63-66",major,dedup / data loss,"A preprint is flagged e5_duplicate whenever a same-title published record exists, even when that published record is itself excluded (e1 window, e2 type, e7 deposit, NULL eligible): both versions are then excluded and the work disappears.",Compute dup_pairs only against published records that pass e1-e4 and e7 (or transfer eligibility to the preprint when the published record fails); count the affected works.
++systematic_review/define_corpus.py,36-43,major,dedup / protocol mismatch,"Preprint-published merge relies on exact normalised title + |year|<=2 only; the arXiv-id relation (link_arxiv_oa: two OA records sharing one arXiv id) and DOI relations are not used, retitled VoRs stay duplicated, preprint->preprint pairs (SSRN->arXiv) are ignored, and the `version_relationship` field announced in plan S2 is not produced.","Add arXiv-id-based pairs, a fuzzy title fallback (token Jaccard >= 0.9 + first author), produce version_relationship, and report the duplicate rate by year (P5)."
++systematic_review/define_corpus.py,"50,56",major,date edge case,"first_date = least(publication_date, preprint_date, arxiv_created): a year-precision publication_date (YYYY-01-01) wins over a precise later arXiv date, biasing first_date to January and mis-assigning `period`/`first_month`; moreover date_precision is defined on publication_date, not on first_date, which the monthly series actually use.",Treat year-precision publication dates as NULL inside least() when a precise date exists; define date_precision on the date actually chosen for first_date.
++systematic_review/define_corpus.py,"16,51",major,provenance / protocol mismatch,"WINDOW_END = 2026-11-02 while the works tables come from the 2026-10-14 snapshot; the API increment (openalex_api_increment.py) writes raw pages that no script converts into the tables (build_corpus reads only cs/*.jsonl.zst, compact_tables only tables/tables_p2). The effective end of the corpus is 2026-10-14 minus indexing lag, not 2026-11-02 as stated in prisma_counts.json and PROTOCOL P3.",Either add an increment ingestion path (pages -> tables_inc -> compact) or log an amendment stating the effective data cut-off (2026-10-14) and set WINDOW_END accordingly.
++systematic_review/define_corpus.py,"51,84",minor,PRISMA reporting,NULL publication_date is counted as E1 'outside window'.,Add a separate reason code (missing date).
++systematic_review/define_corpus.py,33,minor,nondeterminism,"any_value(arxiv_id), any_value(primary_category) when one OA work links to several arXiv ids.","Use arg_min(arxiv_id, created) / deterministic ordering."
++systematic_review/define_corpus.py,65-69,minor,consistency,Eligible works with period='pre' (preprint before 2018) are counted in yearly tables (publication_year) but excluded from monthly series (first_month < 2018-01): yearly and monthly totals differ.,Decide (exclude period='pre' or use the year of first_date) and document.
++systematic_review/define_corpus.py,67-68,minor,boundary,Baseline/post boundary 2022-11-30 (day level) vs econometrics event 2022-12-01 (month level): the 2022-11 month is pre-event in ITS.,State the one-day difference in the methods section.
++econometrics/structural_change.py,43-49,major,protocol mismatch,"Placebo candidate months run over the whole sample (min_pre .. n-min_post): 30 of 66 placebos are post-event in the default window, each absorbing part of the true break, and pre-event placebos are estimated on a sample containing the true break. Protocol P6 / plan 6 say 'placebo breaks at every month of the baseline' (verified: placebo p 0.015 vs 0.0 on the synthetic test series).",Restrict candidates to the baseline and fit placebo ITS on pre-event data only (or include the true-event dummies as controls); report both.
++econometrics/structural_change.py,61-101,major,inference gap,"Bai-Perron = global SSR minimiser with BIC/LWZ (DP verified against brute force) but no supF / UDmax / F(l+1|l) tests, no critical values, no seasonal regressors: H3 ('no break at the 5 % level') cannot be evaluated as pre-registered.","Add supF(k) and sequential F tests with Bai-Perron (2003) critical values at trimming 0.15, or a parametric bootstrap p-value for the BIC-selected break; add month dummies as fixed regressors."
++econometrics/structural_change.py,32-36,minor,documentation,"Fixed Newey-West maxlags=6 (automatic rule gives 4 for n~100); design matrix (const, t, post, t_post, 11 month dummies) verified correct.","State the bandwidth and add a lag sensitivity (4, 6, 12)."
++econometrics/structural_change.py,"64,92-100",minor,convention,"Breaks reported as the first index of the new regime (BP convention: last index of the old regime); h = max(8, 0.15 n).",Document the convention to avoid a one-month misreading.
++econometrics/structural_change.py,104-109,minor,method,"PELT penalty 2 log(n) var(y) uses the full-sample variance (inflated by the break), making PELT conservative.",Use a robust scale (MAD of first differences) for the penalty.
++econometrics/structural_change.py,112-135,minor,documentation,BOCPD prior mu0/beta0 taken from the first 6 observations (data-dependent); hazard 1/60 fixed; update equations verified.,Document; add a hazard sensitivity.
++econometrics/run_structural_change.py,"36,44",minor,method,log(n+1) transformation and Bai-Perron on the unadjusted log series (no seasonal adjustment) for category series with small counts.,Add a Poisson/NB ITS robustness and deseasonalise before Bai-Perron/PELT.
++econometrics/run_structural_change.py,53,minor,provenance,Default --first 2018-02 drops January 2018 without stated reason.,Document or start at 2018-01.
++econometrics/run_structural_change.py,63,minor,logic / hygiene,`out + '/sens_tmp' if os.makedirs(...) is None else out` always evaluates to sens_tmp; sensitivity runs overwrite each other's placebo_total.csv/fit_total.csv.,Write sensitivity outputs with the cut-off in the file name.
++econometrics/run_structural_change.py,56-67,minor,provenance,"its_results.json carries no input hashes, git commit or environment stamp.","Add a provenance block (input file sha256, commit, package versions)."
++bibliometrics/01_volume_series.py,22-27,major,plan mismatch / right-truncation,Indexing-lag table is produced but no last-complete-month estimate or completeness correction is derived from it (plan 6: 'right-truncation corrected by indexing-delay profiles'); the econometrics driver uses a hard-coded --last-complete 2026-06.,"Estimate per-month completeness from the previous year's lag profile, write last_complete_month + correction factors to a JSON consumed by run_structural_change.py and emerging_detection.py."
++bibliometrics/01_volume_series.py,22-27,minor,data quality,Indexing lag computed on all eligible records including year-precision dates (Jan-1 default) -> biased lag distribution.,Restrict to date_precision = 'month'.
++bibliometrics/01_volume_series.py,13,minor,consistency,"Monthly counts keyed on first_month (preprint date) while date_precision refers to publication_date (see define_corpus 50,56).",Fix upstream; document.
++bibliometrics/02_collaboration.py,19-20,minor,definition,"Fractional country counting = 1/#distinct countries per work (work-level set), not author-fractional.",State the counting rule; optionally add author-fractional counts from oa_authorships.
++bibliometrics/02_collaboration.py,"21,24-25",minor,coverage,"share_intl and share_company denominators are works with affiliation data; affiliation coverage varies by year and source, which can drive H6 trends.",Add coverage columns (share of works with >= 1 country / institution) per year.
++bibliometrics/02_collaboration.py,14,minor,documentation,Includes retracted and period='pre' works.,Document.
++knowledge_graph/02_category_flows.py,28-30,major,coverage,"INNER JOIN on work_primary drops unclassified works from flows; row_share and cross-area shares are conditional on classification, and the unclassified share may vary by year/area.",Add an 'unclassified' pseudo-category and report its share per year.
++knowledge_graph/02_category_flows.py,"13-17,52",minor,verified / inference,Directed weighted modularity formula verified against networkx (identical); no bootstrap CI for Q or cross-area shares although protocol P6 requires uncertainty on all estimates.,Add edge-resampling bootstrap CIs.
++knowledge_graph/02_category_flows.py,40-41,minor,consistency,n_month_precision uses date_precision (publication_date) while the month is first_month.,Fix upstream (define_corpus).
++knowledge_graph/01_citation_graph.py,23-26,minor,coverage,References whose target is absent from the slim table (merged/deleted OpenAlex ids) are silently dropped; the OpenAlex merged_ids list is not used to remap them.,"Download merged_ids/works, remap, and report n_dangling per year."
++knowledge_graph/01_citation_graph.py,31-32,minor,date edge case,Negative citation ages (cited_year > citing_year) are kept.,Flag/clip and report their share.
++ingestion/link_sources.py,"36,50-53",major,dead code path,"DBLP arXiv linkage yields 0 links (linkage_report.json: dblp_with_arxiv = 0) because DBLP CoRR `ee` now holds https://doi.org/10.48550/arXiv.<id>, not arxiv.org/abs/; the method is dead and the DBLP-arXiv version relation is unavailable to the merge step.",Derive arxiv_id from the DBLP DOI (10.48550/arxiv.<id>) as well; re-run and report the method counts.
++ingestion/link_sources.py,"54-60,67-72,81-83",major,protocol mismatch / precision,"title+year linkage without the first-author surname check specified in plan S1; generic titles >= 25 chars can be mislinked (180k DBLP, 2.1k arXiv, 14.9k ACL links).",Add the first-author surname (DBLP authors[0] vs OpenAlex first authorship) and report precision by method in the H-1 sample.
++ingestion/link_sources.py,"52-53,66,72,80,83",minor,fragility,NOT IN (SELECT ...) predicates become NULL (no rows) if any key is NULL; currently safe but fragile.,Use NOT EXISTS.
++ingestion/link_sources.py,85-118,minor,provenance,linkage_report.json does not record which pass (1 or 2) produced it; the committed file is the pass-1 state.,Add a `pass` field and the input file hashes.
++ingestion/build_corpus.py,69-76,minor,dead code / inconsistency,ids['arxiv'] does not exist in the OpenAlex schema (dead branch); DOI branch keeps the version suffix while the URL branch strips it (normalised later in link_sources).,Remove the dead branch; strip v\d+ in all branches.
++ingestion/build_corpus.py,144,minor,logic,is_primary location inferred by URL equality -> zero or several primaries when URLs are NULL.,"Compare on source id + landing/pdf URL, or store the primary location separately."
++ingestion/build_corpus.py,173-176,minor,provenance,"Parquet outputs carry no metadata (snapshot release, code commit).",Write key/value metadata into the Parquet schema.
++ingestion/openalex_snapshot.py,148-157,minor,robustness,"If a partial download is larger than expected, `curl -C -` keeps resuming it and the part fails forever (status error on every run).",Delete raw_path on size mismatch before retrying.
++ingestion/openalex_snapshot.py,160-164,minor,provenance,SHA-256 is recorded but never compared to an upstream value (manifest has no hash).,Record the S3 ETag from the HTTP headers and compare; state that the hash is provenance only.
++ingestion/openalex_snapshot.py,188-189,minor,PRISMA reporting,Works with NULL publication_year are excluded from the CS subset without being counted.,Count n_null_year in the ledger for stage S0.
++ingestion/openalex_snapshot.py,236-239,minor,provenance,runs table stores argv/host but not the code commit or environment.,Add git commit and environment file hash.
++taxonomy/classify.py,108,minor,nondeterminism,row_number() OVER () without ORDER BY: chunk composition varies across runs (per-chunk files not byte-reproducible; aggregates unaffected).,ORDER BY id.
++taxonomy/classify.py,"120-129,142-143",minor,provenance,Shard mode writes no classification_report.json; outputs are not version-stamped although the docstring says so.,Write ontology version + sha256(ontology.yaml) into Parquet metadata or a sidecar in both modes.
++taxonomy/classify.py,146-157,minor,robustness,input_named symlinks persist across --prepare re-runs; stale links short-circuit os.path.exists.,Clear input_named in prepare().
++taxonomy/classify.py,"28,55-57",minor,robustness,Keyword regexes compiled without flags; 273 of 277 ontology patterns carry (?i) - any pattern without it is case-sensitive.,Compile with re.I or add a lint test that every pattern starts with (?i).
++experiments/emerging_detection.py,"27-28,22-23",major,protocol gap,KNOWN_SET is empty and the lead-time evaluation on the preregistered known-emergence set (H8; known_set_lead_times.csv in the docstring) is not implemented.,Fill experiments/known_set.csv (topic ids per preregistered item) and implement lead time = first cutoff where score rank <= k minus peak-growth month.
++experiments/emerging_detection.py,"78,83-86,66-74",major,right-truncation / separation,Panel ends --last-month 2026-10 (incomplete months) while econometrics uses 2026-06: for h=24 the last test labels use volumes up to 2026-10 (truncated) and the last train label window (cutoff 2021-10 + 24 = 2023-10) overlaps the test feature period starting 2023-01.,Require T + h <= last_complete_month for labels and last_train_cutoff + h <= first_test_cutoff; read the last complete month from the volume-series JSON.
++experiments/emerging_detection.py,31-38,major,look-ahead (unit definition),"Units are OpenAlex topics, a taxonomy built in 2024 that already knows which topics emerged (e.g. an LLM topic exists as a unit at 2020 cutoffs); the plan's cutoff-specific semantic clusters (experiments/emerging_clusters.py) do not exist. Features themselves are look-ahead-safe (verified: invariant to perturbation of columns > T).","Implement cutoff-specific clustering on embeddings (or keyword n-grams) as the primary unit, keep topics as a sensitivity, and declare the unit-definition look-ahead in the manuscript."
++experiments/emerging_detection.py,40-41,minor,look-ahead (mild),Topic universe (>= 50 works over 2018-2026) selected with full-period data.,"Select units per cutoff (>= 20 works in (T-12, T]) or document."
++experiments/emerging_detection.py,56-57,minor,documentation,Trailing window for zvol is 25 months (T-27..T-3); docstring says 24.,"Use n[:, T-26:T-2] or fix the docstring."
++experiments/emerging_detection.py,85,minor,documentation,Train cutoffs are 2020-01 ... 2021-10 (quarterly on index % 3); docstring says cutoffs <= 2021-12.,Align docstring/protocol or define cutoffs explicitly.
++experiments/emerging_detection.py,"98,108-113",minor,method,"Logistic regression on unscaled features (share, age); pooled AUC mixes cutoffs with different base rates; no bootstrap CI (P6).",Add StandardScaler in a pipeline; report per-cutoff metrics with bootstrap CIs.
++embeddings/semantic_atlas.py,"4,66",minor,documentation,Docstring says stratified sample; code draws a simple random sample.,"Stratify by (period, primary_category) or correct the docstring."
++embeddings/semantic_atlas.py,81-87,minor,method,"Stability = split-half ARI with kNN transfer inside one UMAP fitted on the full sample (not a bootstrap, and UMAP not refitted) -> overstated stability.",Refit UMAP+HDBSCAN per half (or per bootstrap resample) and report both.
++embeddings/semantic_atlas.py,49-61,minor,data quality,Novelty centroids use first_month including year-precision records (January centroids polluted).,Restrict to date_precision = 'month'.
++embeddings/semantic_atlas.py,30,minor,robustness,Duplicate work ids across shards resolved silently by last occurrence.,Assert uniqueness of ids.
++embeddings/semantic_atlas.py,"37,58",minor,verified,"Cosine via dot product is valid only because embed_corpus.py normalises (normalize_embeddings=True, verified); float16 storage.",Assert unit norms after loading.
++embeddings/semantic_atlas.py,41,minor,boundary,Baseline/post defined by calendar year (2018-2022 vs 2023-2026) instead of the period column (Dec 2022 is post).,Use c.period.
++embeddings/semantic_atlas.py,"67-69,88",minor,provenance,UMAP/HDBSCAN results depend on umap-learn/numba/pynndescent versions; stability.json has no version stamp.,Record package versions and the embedding model tag in outputs.

spb/wp44_uqo

WP44: Part IV methodology (Agent L), discrepancy register, A-005

Theme