1key,doi_or_arxiv,verified_via,verified_title,match,note2priem2022openalex,arXiv:2205.01833,arxiv,"OpenAlex: A fully-open index of scholarly works, authors, venues, institutions, and concepts",exact,sim=1.00; year=2022; nauth=3 [group A]3ley2002dblp,10.1007/3-540-45735-6_1,crossref,"The DBLP Computer Science Bibliography: Evolution, Research Issues, Perspectives",exact,sim=1.00; year=2002; nauth=1 [group A]4ley2009dblp,10.14778/1687553.1687577,crossref,DBLP: some lessons learned,exact,sim=1.00; year=2009; nauth=1 [group A]5ginsparg2011arxiv,10.1038/476145a,crossref,ArXiv at 20,exact,sim=1.00; year=2011; nauth=1 [group A]6kinney2023s2ag,arXiv:2301.10140,arxiv,The Semantic Scholar Open Data Platform,exact,sim=1.00; year=2023; nauth=48 [group A]7lo2020s2orc,10.18653/v1/2020.acl-main.447,crossref,S2ORC: The Semantic Scholar Open Research Corpus,exact,sim=1.00; year=2020; nauth=5 [group A]8ammar2018literaturegraph,10.18653/v1/N18-3011,crossref,Construction of the Literature Graph in Semantic Scholar,exact,sim=1.00; year=2018; nauth=23 [group A]9hendricks2020crossref,10.1162/qss_a_00022,crossref,Crossref: The sustainable source of community-owned scholarly metadata,exact,sim=1.00; year=2020; nauth=4 [group A]10radev2013aclanthology,10.1007/s10579-012-9211-2,crossref,The ACL anthology network corpus,exact,sim=1.00; year=2013; nauth=4 [group A]11wang2020mag,10.1162/qss_a_00021,crossref,Microsoft Academic Graph: When experts are not enough,exact,sim=1.00; year=2020; nauth=6 [group A]12sinha2015mas,10.1145/2740908.2742839,crossref,An Overview of Microsoft Academic Service (MAS) and Applications,exact,sim=1.00; year=2015; nauth=7 [group A]13giles1998citeseer,10.1145/276675.276685,crossref,CiteSeer: an automatic citation indexing system,exact,sim=1.00; year=1998; nauth=3 [group A]14wu2015citeseerx,10.1609/aimag.v36i3.2601,crossref,CiteSeerX: AI in a Digital Library Search Engine,exact,sim=1.00; year=2015; nauth=10 [group A]15tang2008arnetminer,10.1145/1401890.1402008,crossref,ArnetMiner: extraction and mining of academic social networks,exact,sim=1.00; year=2008; nauth=6 [group A]16fricke2018semanticscholar,10.5195/jmla.2018.280,crossref,Semantic Scholar,exact,sim=1.00; year=2018; nauth=1 [group A]17herzog2020dimensions,10.1162/qss_a_00020,crossref,Dimensions: Bringing down barriers between scientometricians and data,exact,sim=1.00; year=2020; nauth=3 [group A]18peroni2020opencitations,10.1162/qss_a_00023,crossref,"OpenCitations, an infrastructure organization for open scholarship",minor,sim=0.89; year=2020; nauth=2 [group A]19visser2021comparison,10.1162/qss_a_00112,crossref,"Large-scale comparison of bibliographic data sources: Scopus, Web of Science, Dimensions, Crossref, and Microsoft Academic",exact,sim=1.00; year=2021; nauth=3 [group A]20martinmartin2021coverage,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,sim=1.00; year=2021; nauth=4 [group A]21clement2019arxivdataset,arXiv:1905.00075,arxiv,On the Use of ArXiv as a Dataset,exact,sim=1.00; year=2019; nauth=4 [group A]22alperin2024openalex,arXiv:2404.17663,arxiv,An analysis of the suitability of OpenAlex for bibliometric analyses,exact,sim=1.00; year=2024; nauth=5 [group A]23culbert2025openalexcoverage,10.1007/s11192-025-05293-3,crossref,Reference coverage analysis of OpenAlex compared to Web of Science and Scopus,exact,sim=1.00; year=2025; nauth=7 [group A]24scheidsteger2023magopenalex,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,sim=1.00; year=2023; nauth=2 [group A]25openalex2024topics,https://docs.openalex.org/api-entities/topics,url,Topics – Aboutness | OpenAlex Help Center,minor,HTTP 200; page title similarity 0.60 [group A]26paperswithcode2024,https://paperswithcode.com/,url,Trending Papers - Hugging Face,FAILED,site retired in 2025; URL now redirects to huggingface.co/papers (page title 'Trending Papers - Hugging Face'); not citable as a live source HTTP 200; page title similarity 0.50 [group A]27dblp2024stats,https://dblp.org/statistics/,url,Making sure you're not a bot!,FAILED,HTTP 202; page title similarity 0.27 [group A]28arxiv2026stats,https://arxiv.org/stats/monthly_submissions,url,Monthly Submissions,minor,HTTP 200; page title similarity 0.70 [group A]29acl2024anthology,https://aclanthology.org/,url,ACL Anthology,exact,HTTP 200; page title similarity 1.00 [group A]30haunschild2024openalexclassification,-,,,FAILED,crossref search: no title match >=0.85 [group A]31page2021prisma,10.1136/bmj.n71,crossref,The PRISMA 2020 statement: an updated guideline for reporting systematic reviews,exact,sim=1.00; year=2021; nauth=26 [group B]32page2021prismaexplanation,10.1136/bmj.n160,crossref,PRISMA 2020 explanation and elaboration: updated guidance and exemplars for reporting systematic reviews,exact,sim=1.00; year=2021; nauth=26 [group B]33rethlefsen2021prismas,10.1186/s13643-020-01542-z,crossref,PRISMA-S: an extension to the PRISMA Statement for Reporting Literature Searches in Systematic Reviews,exact,sim=1.00; year=2021; nauth=41 [group B]34moher2009prisma,10.1371/journal.pmed.1000097,crossref,Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement,exact,sim=1.00; year=2009; nauth=5 [group B]35haddaway2022prismaflow,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,sim=1.00; year=2022; nauth=4 [group B]36kitchenham2009slr,10.1016/j.infsof.2008.09.009,crossref,Systematic literature reviews in software engineering – A systematic literature review,exact,sim=1.00; year=2009; nauth=6 [group B]37kitchenham2013slrprocess,10.1016/j.infsof.2013.07.010,crossref,A systematic review of systematic review process research in software engineering,exact,sim=1.00; year=2013; nauth=2 [group B]38kitchenham2015ebse,10.1201/b19467,crossref,Evidence-Based Software Engineering and Systematic Reviews,exact,sim=1.00; year=2015; nauth=3 [group B]39petersen2008mapping,10.14236/ewic/EASE2008.8,crossref,Systematic Mapping Studies in Software Engineering,exact,sim=1.00; year=2008; nauth=4 [group B]40petersen2015mapping,10.1016/j.infsof.2015.03.007,crossref,Guidelines for conducting systematic mapping studies in software engineering: An update,exact,sim=1.00; year=2015; nauth=3 [group B]41wohlin2014snowballing,10.1145/2601248.2601268,crossref,Guidelines for snowballing in systematic literature studies and a replication in software engineering,exact,sim=1.00; year=2014; nauth=1 [group B]42garousi2019multivocal,10.1016/j.infsof.2018.09.006,crossref,Guidelines for including grey literature and conducting multivocal literature reviews in software engineering,exact,sim=1.00; year=2019; nauth=3 [group B]43tricco2018prismascr,10.7326/M18-0850,crossref,PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation,exact,sim=1.00; year=2018; nauth=28 [group B]44arksey2005scoping,10.1080/1364557032000119616,crossref,Scoping studies: towards a methodological framework,exact,sim=1.00; year=2005; nauth=2 [group B]45grant2009typology,10.1111/j.1471-1842.2009.00848.x,crossref,A typology of reviews: an analysis of 14 review types and associated methodologies,exact,sim=1.00; year=2009; nauth=2 [group B]46elliott2014living,10.1371/journal.pmed.1001603,crossref,Living Systematic Reviews: An Emerging Opportunity to Narrow the Evidence-Practice Gap,exact,sim=1.00; year=2014; nauth=7 [group B]47elliott2017living,10.1016/j.jclinepi.2017.08.010,crossref,"Living systematic review: 1. Introduction—the why, what, when, and how",exact,sim=1.00; year=2017; nauth=144 [group B]48vandeschoot2021asreview,10.1038/s42256-020-00287-7,crossref,An open source machine learning framework for efficient and transparent systematic reviews,exact,sim=1.00; year=2021; nauth=17 [group B]49omaraeide2015textmining,10.1186/2046-4053-4-5,crossref,Using text mining for study identification in systematic reviews: a systematic review of current approaches,exact,sim=1.00; year=2015; nauth=5 [group B]50marshall2019automation,10.1186/s13643-019-1074-9,crossref,Toward systematic review automation: a practical guide to using machine learning tools in research synthesis,exact,sim=1.00; year=2019; nauth=2 [group B]51khraisha2024gpt4screening,10.1002/jrsm.1715,crossref,Can large language models replace humans in systematic reviews? Evaluating GPT ‐4's efficacy in screening and extracting data from peer‐reviewed and grey literature in multiple languages,exact,sim=1.00; year=2024; nauth=5 [group B]52guo2024llmscreening,10.2196/48996,crossref,Automated Paper Screening for Clinical Reviews Using Large Language Models: Data Analysis Study,exact,sim=1.00; year=2024; nauth=6 [group B]53syriani2023chatgptscreening,arXiv:2307.06464,arxiv,Assessing the Ability of ChatGPT to Screen Articles for Systematic Reviews,exact,sim=1.00; year=2023; nauth=3 [group B]54dennstadt2024llmscreening,10.1186/s13643-024-02575-4,crossref,Title and abstract screening for literature reviews using large language models: an exploratory study in the biomedical domain,exact,sim=1.00; year=2024; nauth=5 [group B]55alshami2023chatgptsr,10.3390/systems11070351,crossref,"Harnessing the Power of ChatGPT for Automating Systematic Review Process: Methodology, Case Study, Limitations, and Future Directions",exact,sim=1.00; year=2023; nauth=5 [group B]56zupic2015bibliometric,10.1177/1094428114562629,crossref,Bibliometric Methods in Management and Organization,exact,sim=1.00; year=2015; nauth=2 [group B]57donthu2021bibliometric,10.1016/j.jbusres.2021.04.070,crossref,How to conduct a bibliometric analysis: An overview and guidelines,exact,sim=1.00; year=2021; nauth=5 [group B]58wang2024llmsr,-,,,FAILED,crossref search: no title match >=0.85 [group B]59wang2024zeroshotscreening,10.1007/978-3-031-56027-9_25,crossref,Zero-Shot Generative Large Language Models for Systematic Review Screening Automation,exact,sim=1.00; year=2024; nauth=6 [group B]60waltman2016review,10.1016/j.joi.2016.02.007,crossref,A review of the literature on citation impact indicators,exact,sim=1.00; year=2016; nauth=1 [group C]61waltman2019fieldnormalization,10.1007/978-3-030-02511-3_11,crossref,Field Normalization of Scientometric Indicators,exact,sim=1.00; year=2019; nauth=2 [group C]62waltman2011crown,10.1016/j.joi.2010.08.001,crossref,Towards a new crown indicator: Some theoretical considerations,exact,sim=1.00; year=2011; nauth=5 [group C]63purkayastha2019fwci,10.1016/j.joi.2019.03.012,crossref,"Comparison of two article-level, field-independent citation metrics: Field-Weighted Citation Impact (FWCI) and Relative Citation Ratio (RCR)",exact,sim=1.00; year=2019; nauth=4 [group C]64wang2013citationwindow,10.1007/s11192-012-0775-9,crossref,Citation time window choice for research impact evaluation,exact,sim=1.00; year=2013; nauth=1 [group C]65hirsch2005hindex,10.1073/pnas.0507655102,crossref,An index to quantify an individual's scientific research output,exact,sim=1.00; year=2005; nauth=1 [group C]66waltman2012hindex,10.1002/asi.21678,crossref,The inconsistency of the h‐index,exact,sim=1.00; year=2012; nauth=2 [group C]67egghe2006gindex,10.1007/s11192-006-0144-7,crossref,Theory and practise of the g-index,exact,sim=1.00; year=2006; nauth=1 [group C]68nielsen2021inequality,10.1073/pnas.2012208118,crossref,Global citation inequality is on the rise,exact,sim=1.00; year=2021; nauth=2 [group C]69stirling2007diversity,10.1098/rsif.2007.0213,crossref,"A general framework for analysing diversity in science, technology and society",exact,sim=1.00; year=2007; nauth=1 [group C]70leinster2012diversity,10.1890/10-2402.1,crossref,Measuring diversity: the importance of species similarity,exact,sim=1.00; year=2012; nauth=2 [group C]71porter2009interdisciplinarity,10.1007/s11192-008-2197-2,crossref,Is science becoming more interdisciplinary? Measuring and mapping six research fields over time,exact,sim=1.00; year=2009; nauth=2 [group C]72rafols2010diversity,10.1007/s11192-009-0041-y,crossref,Diversity and network coherence as indicators of interdisciplinarity: case studies in bionanoscience,exact,sim=1.00; year=2010; nauth=2 [group C]73leydesdorff2019interdisciplinarity,10.1016/j.joi.2018.12.006,crossref,"Interdisciplinarity as diversity in citation patterns among journals: Rao-Stirling diversity, relative variety, and the Gini coefficient",exact,sim=1.00; year=2019; nauth=3 [group C]74newman2001collaboration,10.1073/pnas.021544898,crossref,The structure of scientific collaboration networks,exact,sim=1.00; year=2001; nauth=1 [group C]75newman2004coauthorship,10.1073/pnas.0307545100,crossref,Coauthorship networks and patterns of scientific collaboration,exact,sim=1.00; year=2004; nauth=1 [group C]76wagner2017collaboration,10.1007/s11192-016-2230-9,crossref,Growth of international collaboration in science: revisiting six specialties,minor,sim=0.96; year=2017; nauth=3 [group C]77wagner2015cooperation,10.1371/journal.pone.0131816,crossref,The Continuing Growth of Global Cooperation Networks in Research: A Conundrum for National Governments,exact,sim=1.00; year=2015; nauth=3 [group C]78katz1997collaboration,10.1016/S0048-7333(96)00917-1,crossref,What is research collaboration?,exact,sim=1.00; year=1997; nauth=2 [group C]79lariviere2015teamsize,10.1002/asi.23266,crossref,Team size matters: Collaboration and scientific impact since 1900,exact,sim=1.00; year=2015; nauth=4 [group C]80wuchty2007teams,10.1126/science.1136099,crossref,The Increasing Dominance of Teams in Production of Knowledge,exact,sim=1.00; year=2007; nauth=3 [group C]81lin2020arxivcs,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,minor,sim=0.90; year=2020; nauth=5 [group C]82fraser2021preprints,10.1371/journal.pbio.3000959,crossref,The evolving role of preprints in the dissemination of COVID-19 research and their impact on the science communication landscape,exact,sim=1.00; year=2021; nauth=7 [group C]83fraser2020biorxiv,10.1162/qss_a_00043,crossref,"The relationship between bioRxiv preprints, citations and altmetrics",exact,sim=1.00; year=2020; nauth=4 [group C]84lariviere2014arxiv,10.1002/asi.23044,crossref,arXiv E‐prints and the journal of record: An analysis of roles and relationships,exact,sim=1.00; year=2014; nauth=6 [group C]85sutton2017arxivcs,arXiv:1710.05225,arxiv,Popularity of arXiv.org within Computer Science,exact,sim=1.00; year=2017; nauth=2 [group C]86piwowar2018oa,10.7717/peerj.4375,crossref,The state of OA: a large-scale analysis of the prevalence and impact of Open Access articles,exact,sim=1.00; year=2018; nauth=9 [group C]87bornmann2008citations,10.1108/00220410810844150,crossref,What do citation counts measure? A review of studies on citing behavior,exact,sim=1.00; year=2008; nauth=2 [group C]88radicchi2008universality,10.1073/pnas.0806977105,crossref,Universality of citation distributions: Toward an objective measure of scientific impact,exact,sim=1.00; year=2008; nauth=3 [group C]89hicks2015leiden,10.1038/520429a,crossref,Bibliometrics: The Leiden Manifesto for research metrics,exact,sim=1.00; year=2015; nauth=5 [group C]90price1965networks,10.1126/science.149.3683.510,crossref,Networks of Scientific Papers: The pattern of bibliographic references indicates the nature of the scientific research front.,exact,sim=1.00; year=1965; nauth=1 [group C]91bornmann2015growth,10.1002/asi.23329,crossref,Growth rates of modern science: A bibliometric analysis based on the number of publications and cited references,exact,sim=1.00; year=2015; nauth=2 [group C]92bornmann2021growth,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,sim=1.00; year=2021; nauth=3 [group C]93fire2019goodhart,10.1093/gigascience/giz053,crossref,Over-optimization of academic publishing metrics: observing Goodhart's Law in action,exact,sim=1.00; year=2019; nauth=2 [group C]94ioannidis2020citation,10.1371/journal.pbio.3000918,crossref,Updated science-wide author databases of standardized citation indicators,exact,sim=1.00; year=2020; nauth=3 [group C]95sinatra2016impact,10.1126/science.aaf5239,crossref,Quantifying the evolution of individual scientific impact,exact,sim=1.00; year=2016; nauth=5 [group C]96fanelli2016publicationrate,10.1371/journal.pone.0149504,crossref,Researchers’ Individual Publication Rate Has Not Increased in a Century,exact,sim=1.00; year=2016; nauth=2 [group C]97garfield1955citation,10.1126/science.122.3159.108,crossref,Citation Indexes for Science: A New Dimension in Documentation through Association of Ideas,exact,sim=1.00; year=1955; nauth=1 [group C]98vaneck2010vosviewer,10.1007/s11192-009-0146-3,crossref,"Software survey: VOSviewer, a computer program for bibliometric mapping",exact,sim=1.00; year=2010; nauth=2 [group D]99vaneck2014visualizing,10.1007/978-3-319-10377-8_13,crossref,Visualizing Bibliometric Networks,exact,sim=1.00; year=2014; nauth=2 [group D]100small1973cocitation,10.1002/asi.4630240406,crossref,Co‐citation in the scientific literature: A new measure of the relationship between two documents,exact,sim=1.00; year=1973; nauth=1 [group D]101kessler1963coupling,10.1002/asi.5090140103,crossref,Bibliographic coupling between scientific papers,exact,sim=1.00; year=1963; nauth=1 [group D]102boyack2010accuracy,10.1002/asi.21419,crossref,"Co‐citation analysis, bibliographic coupling, and direct citation: Which citation approach represents the research front most accurately?",exact,sim=1.00; year=2010; nauth=2 [group D]103klavans2017taxonomy,10.1002/asi.23734,crossref,Which Type of Citation Analysis Generates the Most Accurate Taxonomy of Scientific and Technical Knowledge?,exact,sim=1.00; year=2017; nauth=2 [group D]104traag2019leiden,10.1038/s41598-019-41695-z,crossref,From Louvain to Leiden: guaranteeing well-connected communities,exact,sim=1.00; year=2019; nauth=3 [group D]105blondel2008louvain,10.1088/1742-5468/2008/10/P10008,crossref,Fast unfolding of communities in large networks,exact,sim=1.00; year=2008; nauth=4 [group D]106newman2006modularity,10.1073/pnas.0601602103,crossref,Modularity and community structure in networks,exact,sim=1.00; year=2006; nauth=1 [group D]107fortunato2010community,10.1016/j.physrep.2009.11.002,crossref,Community detection in graphs,exact,sim=1.00; year=2010; nauth=1 [group D]108waltman2010unified,10.1016/j.joi.2010.07.002,crossref,A unified approach to mapping and clustering of bibliometric networks,exact,sim=1.00; year=2010; nauth=3 [group D]109waltman2012classification,10.1002/asi.22748,crossref,A new methodology for constructing a publication‐level classification system of science,exact,sim=1.00; year=2012; nauth=2 [group D]110subelj2016clustering,10.1371/journal.pone.0154404,crossref,Clustering Scientific Publications Based on Citation Relations: A Systematic Comparison of Different Methods,exact,sim=1.00; year=2016; nauth=3 [group D]111sjogarde2018granularity,10.1016/j.joi.2017.12.006,crossref,Granularity of algorithmically constructed publication-level classifications of research publications: Identification of topics,exact,sim=1.00; year=2018; nauth=2 [group D]112chen2006citespace,10.1002/asi.20317,crossref,CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature,exact,sim=1.00; year=2006; nauth=1 [group D]113borner2003domains,10.1002/aris.1440370106,crossref,Visualizing knowledge domains,exact,sim=1.00; year=2003; nauth=3 [group D]114leydesdorff2009globalmap,10.1002/asi.20967,crossref,A global map of science based on the ISI subject categories,exact,sim=1.00; year=2009; nauth=2 [group D]115cohan2020specter,10.18653/v1/2020.acl-main.207,crossref,SPECTER: Document-level Representation Learning using Citation-informed Transformers,exact,sim=1.00; year=2020; nauth=5 [group D]116singh2023scirepeval,10.18653/v1/2023.emnlp-main.338,crossref,SciRepEval: A Multi-Format Benchmark for Scientific Document Representations,exact,sim=1.00; year=2023; nauth=5 [group D]117ostendorff2022scincl,10.18653/v1/2022.emnlp-main.802,crossref,Neighborhood Contrastive Learning for Scientific Document Representations with Citation Embeddings,exact,sim=1.00; year=2022; nauth=5 [group D]118beltagy2019scibert,10.18653/v1/D19-1371,crossref,SciBERT: A Pretrained Language Model for Scientific Text,exact,sim=1.00; year=2019; nauth=3 [group D]119reimers2019sbert,10.18653/v1/D19-1410,crossref,Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks,exact,sim=1.00; year=2019; nauth=2 [group D]120gao2021simcse,10.18653/v1/2021.emnlp-main.552,crossref,SimCSE: Simple Contrastive Learning of Sentence Embeddings,exact,sim=1.00; year=2021; nauth=3 [group D]121muennighoff2023mteb,10.18653/v1/2023.eacl-main.148,crossref,MTEB: Massive Text Embedding Benchmark,exact,sim=1.00; year=2023; nauth=4 [group D]122mcinnes2018umap,arXiv:1802.03426,arxiv,UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction,exact,sim=1.00; year=2018; nauth=3 [group D]123mcinnes2018umapjoss,10.21105/joss.00861,crossref,UMAP: Uniform Manifold Approximation and Projection,exact,sim=1.00; year=2018; nauth=4 [group D]124campello2013hdbscan,10.1007/978-3-642-37456-2_14,crossref,Density-Based Clustering Based on Hierarchical Density Estimates,exact,sim=1.00; year=2013; nauth=3 [group D]125mcinnes2017hdbscan,10.21105/joss.00205,crossref,hdbscan: Hierarchical density based clustering,exact,sim=1.00; year=2017; nauth=3 [group D]126schubert2017dbscan,10.1145/3068335,crossref,"DBSCAN Revisited, Revisited: Why and How You Should (Still) Use DBSCAN",exact,sim=1.00; year=2017; nauth=5 [group D]127grootendorst2022bertopic,arXiv:2203.05794,arxiv,BERTopic: Neural topic modeling with a class-based TF-IDF procedure,exact,sim=1.00; year=2022; nauth=1 [group D]128angelov2020top2vec,arXiv:2008.09470,arxiv,Top2Vec: Distributed Representations of Topics,exact,sim=1.00; year=2020; nauth=1 [group D]129blei2003lda,https://jmlr.org/papers/v3/blei03a.html,url,Latent Dirichlet Allocation,exact,HTTP 200; page title similarity 1.00 [group D]130blei2006dtm,10.1145/1143844.1143859,crossref,Dynamic topic models,exact,sim=1.00; year=2006; nauth=2 [group D]131dieng2020etm,10.1162/tacl_a_00325,crossref,Topic Modeling in Embedding Spaces,exact,sim=1.00; year=2020; nauth=3 [group D]132greene2014stability,10.1007/978-3-662-44848-9_32,crossref,How Many Topics? Stability Analysis for Topic Models,exact,sim=1.00; year=2014; nauth=3 [group D]133roder2015coherence,10.1145/2684822.2685324,crossref,Exploring the Space of Topic Coherence Measures,exact,sim=1.00; year=2015; nauth=3 [group D]134sievert2014ldavis,10.3115/v1/W14-3110,crossref,LDAvis: A method for visualizing and interpreting topics,exact,sim=1.00; year=2014; nauth=2 [group D]135vandermaaten2008tsne,https://jmlr.org/papers/v9/vandermaaten08a.html,url,Visualizing Data using t-SNE,exact,HTTP 200; page title similarity 1.00 [group D]136devlin2019bert,10.18653/v1/N19-1423,crossref,BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding,exact,sim=1.00; year=2019; nauth=4 [group D]137bai1998breaks,10.2307/2998540,crossref,Estimating and Testing Linear Models with Multiple Structural Changes,exact,sim=1.00; year=1998; nauth=2 [group E]138bai2003breaks,10.1002/jae.659,crossref,Computation and analysis of multiple structural change models,exact,sim=1.00; year=2003; nauth=2 [group E]139andrews1993breaks,10.2307/2951764,crossref,Tests for Parameter Instability and Structural Change With Unknown Change Point,exact,sim=1.00; year=1993; nauth=1 [group E]140andrews1994optimal,10.2307/2951753,crossref,Optimal Tests when a Nuisance Parameter is Present Only Under the Alternative,exact,sim=1.00; year=1994; nauth=2 [group E]141chow1960test,10.2307/1910133,crossref,Tests of Equality Between Sets of Coefficients in Two Linear Regressions,exact,sim=1.00; year=1960; nauth=1 [group E]142perron1989crash,10.2307/1913712,crossref,"The Great Crash, the Oil Price Shock, and the Unit Root Hypothesis",exact,sim=1.00; year=1989; nauth=1 [group E]143hansen2001structuralchange,10.1257/jep.15.4.117,crossref,The New Econometrics of Structural Change: Dating Breaks in U.S. Labor Productivity,exact,sim=1.00; year=2001; nauth=1 [group E]144zeileis2002strucchange,10.18637/jss.v007.i02,crossref,strucchange : An R Package for Testing for Structural Change in Linear Regression Models,exact,sim=1.00; year=2002; nauth=4 [group E]145killick2012pelt,10.1080/01621459.2012.737745,crossref,Optimal Detection of Changepoints With a Linear Computational Cost,exact,sim=1.00; year=2012; nauth=3 [group E]146fryzlewicz2014wbs,10.1214/14-AOS1245,crossref,Wild binary segmentation for multiple change-point detection,exact,sim=1.00; year=2014; nauth=1 [group E]147frick2014smuce,10.1111/rssb.12047,crossref,Multiscale Change Point Inference,exact,sim=1.00; year=2014; nauth=3 [group E]148adams2007bocpd,arXiv:0710.3742,arxiv,Bayesian Online Changepoint Detection,exact,sim=1.00; year=2007; nauth=2 [group E]149truong2020ruptures,10.1016/j.sigpro.2019.107299,crossref,Selective review of offline change point detection methods,exact,sim=1.00; year=2020; nauth=3 [group E]150aminikhanghahi2017survey,10.1007/s10115-016-0987-z,crossref,A survey of methods for time series change point detection,exact,sim=1.00; year=2017; nauth=2 [group E]151newey1987hac,10.2307/1913610,crossref,"A Simple, Positive Semi-Definite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix",exact,sim=1.00; year=1987; nauth=2 [group E]152hamilton1989regime,10.2307/1912559,crossref,A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle,exact,sim=1.00; year=1989; nauth=1 [group E]153bernal2017its,10.1093/ije/dyw098,crossref,Interrupted time series regression for the evaluation of public health interventions: a tutorial,exact,sim=1.00; year=2016; nauth=3 [group E]154bernal2018itscontrol,10.1093/ije/dyy135,crossref,The use of controls in interrupted time series studies of public health interventions,exact,sim=1.00; year=2018; nauth=3 [group E]155kontopantelis2015its,10.1136/bmj.h2750,crossref,Regression based quasi-experimental approach when randomisation is not an option: interrupted time series analysis,minor,sim=0.94; year=2015; nauth=5 [group E]156box1975intervention,10.1080/01621459.1975.10480264,crossref,Intervention Analysis with Applications to Economic and Environmental Problems,exact,sim=1.00; year=1975; nauth=2 [group E]157brodersen2015causalimpact,10.1214/14-AOAS788,crossref,Inferring causal impact using Bayesian structural time-series models,exact,sim=1.00; year=2015; nauth=5 [group E]158abadie2010synthetic,10.1198/jasa.2009.ap08746,crossref,Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California’s Tobacco Control Program,exact,sim=1.00; year=2010; nauth=3 [group E]159abadie2021synthetic,10.1257/jel.20191450,crossref,"Using Synthetic Controls: Feasibility, Data Requirements, and Methodological Aspects",exact,sim=1.00; year=2021; nauth=1 [group E]160sun2021eventstudy,10.1016/j.jeconom.2020.09.006,crossref,Estimating dynamic treatment effects in event studies with heterogeneous treatment effects,exact,sim=1.00; year=2021; nauth=2 [group E]161callaway2021did,10.1016/j.jeconom.2020.12.001,crossref,Difference-in-Differences with multiple time periods,exact,sim=1.00; year=2021; nauth=2 [group E]162goodmanbacon2021did,10.1016/j.jeconom.2021.03.014,crossref,Difference-in-differences with variation in treatment timing,exact,sim=1.00; year=2021; nauth=1 [group E]163roth2022pretest,10.1257/aeri.20210236,crossref,Pretest with Caution: Event-Study Estimates after Testing for Parallel Trends,exact,sim=1.00; year=2022; nauth=1 [group E]164roth2023trending,10.1016/j.jeconom.2023.03.008,crossref,What’s trending in difference-in-differences? A synthesis of the recent econometrics literature,exact,sim=1.00; year=2023; nauth=4 [group E]165taylor2018prophet,10.1080/00031305.2017.1380080,crossref,Forecasting at Scale,exact,sim=1.00; year=2018; nauth=2 [group E]166hyndman2018fpp,https://otexts.com/fpp3/,url,Forecasting: Principles and Practice,minor,HTTP 200; page title similarity 0.91 [group E]167rotolo2015emerging,10.1016/j.respol.2015.06.006,crossref,What is an emerging technology?,exact,sim=1.00; year=2015; nauth=3 [group F]168small2014emergence,10.1016/j.respol.2014.02.005,crossref,Identifying emerging topics in science and technology,exact,sim=1.00; year=2014; nauth=3 [group F]169cozzens2010emerging,10.1080/09537321003647396,crossref,Emerging technologies: quantitative identification and measurement,exact,sim=1.00; year=2010; nauth=7 [group F]170wang2018emerging,10.1002/asi.23930,crossref,A bibliometric model for identifying emerging research topics,exact,sim=1.00; year=2018; nauth=1 [group F]171klavans2017prominence,10.1016/j.joi.2017.10.002,crossref,Research portfolio analysis and topic prominence,exact,sim=1.00; year=2017; nauth=2 [group F]172boyack2022forecasting,10.1162/qss_a_00202,crossref,An improved practical approach to forecasting exceptional growth in research,exact,sim=1.00; year=2022; nauth=2 [group F]173ke2015sleepingbeauties,10.1073/pnas.1424329112,crossref,Defining and identifying Sleeping Beauties in science,exact,sim=1.00; year=2015; nauth=4 [group F]174funk2017disruption,10.1287/mnsc.2015.2366,crossref,A Dynamic Network Measure of Technological Change,exact,sim=1.00; year=2017; nauth=2 [group F]175wu2019disruption,10.1038/s41586-019-0941-9,crossref,Large teams develop and small teams disrupt science and technology,exact,sim=1.00; year=2019; nauth=3 [group F]176park2023disruption,10.1038/s41586-022-05543-x,crossref,Papers and patents are becoming less disruptive over time,exact,sim=1.00; year=2023; nauth=3 [group F]177bornmann2020disruptionvalidity,10.1162/qss_a_00068,crossref,Are disruption index indicators convergently valid? The comparison of several indicator variants with assessments by peers,exact,sim=1.00; year=2020; nauth=4 [group F]178leibel2024disruptionreview,10.1007/s11192-023-04873-5,crossref,What do we know about the disruption index in scientometrics? An overview of the literature,exact,sim=1.00; year=2024; nauth=2 [group F]179holst2024disruptionartefacts,arXiv:2402.14583,arxiv,Dataset Artefacts are the Hidden Drivers of the Declining Disruptiveness in Science,exact,sim=1.00; year=2024; nauth=5 [group F]180uzzi2013novelty,10.1126/science.1240474,crossref,Atypical Combinations and Scientific Impact,exact,sim=1.00; year=2013; nauth=4 [group F]181wang2017novelty,10.1016/j.respol.2017.06.006,crossref,Bias against novelty in science: A cautionary tale for users of bibliometric indicators,exact,sim=1.00; year=2017; nauth=3 [group F]182lee2015novelty,10.1016/j.respol.2014.10.007,crossref,Creativity in scientific teams: Unpacking novelty and impact,exact,sim=1.00; year=2015; nauth=3 [group F]183foster2015tradition,10.1177/0003122415601618,crossref,Tradition and Innovation in Scientists’ Research Strategies,exact,sim=1.00; year=2015; nauth=3 [group F]184kleinberg2003burst,10.1023/A:1024940629314,crossref,Bursty and Hierarchical Structure in Streams,exact,sim=1.00; year=2003; nauth=1 [group F]185mane2004bursts,10.1073/pnas.0307626100,crossref,Mapping topics and topic bursts in PNAS,exact,sim=1.00; year=2004; nauth=2 [group F]186small2006growth,10.1007/s11192-006-0132-y,crossref,Tracking and predicting growth areas in science,exact,sim=1.00; year=2006; nauth=1 [group F]187guo2011mixedindicators,10.1007/s11192-011-0433-7,crossref,Mixed-indicators model for identifying emerging research areas,exact,sim=1.00; year=2011; nauth=3 [group F]188shibata2008fronts,10.1016/j.technovation.2008.03.009,crossref,Detecting emerging research fronts based on topological measures in citation networks of scientific publications,exact,sim=1.00; year=2008; nauth=4 [group F]189porter2019emergence,10.1016/j.techfore.2018.04.016,crossref,Emergence scoring to identify frontier R&D topics and key players,exact,sim=1.00; year=2019; nauth=4 [group F]190salatino2017topicsborn,10.7717/peerj-cs.119,crossref,How are topics born? Understanding the research dynamics preceding the emergence of new areas,exact,sim=1.00; year=2017; nauth=3 [group F]191salatino2018augur,10.1145/3197026.3197052,crossref,AUGUR: Forecasting the Emergence of New Research Topics,exact,sim=1.00; year=2018; nauth=3 [group F]192chu2021slowed,10.1073/pnas.2021636118,crossref,Slowed canonical progress in large fields of science,exact,sim=1.00; year=2021; nauth=2 [group F]193lin2023remote,10.1038/s41586-023-06767-1,crossref,Remote collaboration fuses fewer breakthrough ideas,exact,sim=1.00; year=2023; nauth=3 [group F]194bettencourt2009discovery,10.1016/j.joi.2009.03.001,crossref,Scientific discovery and topological transitions in collaboration networks,exact,sim=1.00; year=2009; nauth=3 [group F]195milojevic2015cognitiveextent,10.1016/j.joi.2015.10.005,crossref,Quantifying the cognitive extent of science,exact,sim=1.00; year=2015; nauth=1 [group F]196kleinberg2002burstkdd,10.1145/775047.775061,crossref,Bursty and hierarchical structure in streams,exact,sim=1.00; year=2002; nauth=1 [group F]197vaswani2017attention,arXiv:1706.03762,arxiv,Attention Is All You Need,exact,sim=1.00; year=2017; nauth=8 [group G]198brown2020gpt3,arXiv:2005.14165,arxiv,Language Models are Few-Shot Learners,exact,sim=1.00; year=2020; nauth=31 [group G]199kaplan2020scaling,arXiv:2001.08361,arxiv,Scaling Laws for Neural Language Models,exact,sim=1.00; year=2020; nauth=10 [group G]200hoffmann2022chinchilla,arXiv:2203.15556,arxiv,Training Compute-Optimal Large Language Models,exact,sim=1.00; year=2022; nauth=22 [group G]201ouyang2022instructgpt,arXiv:2203.02155,arxiv,Training language models to follow instructions with human feedback,exact,sim=1.00; year=2022; nauth=20 [group G]202openai2022chatgpt,https://web.archive.org/web/20221130180912/https://openai.com/blog/chatgpt/,url,ChatGPT: Optimizing Language Models for Dialogue,exact,HTTP 200; page title similarity 1.00 [group G]203openai2023gpt4,arXiv:2303.08774,arxiv,GPT-4 Technical Report,exact,sim=1.00; year=2023; nauth=281 [group G]204touvron2023llama,arXiv:2302.13971,arxiv,LLaMA: Open and Efficient Foundation Language Models,exact,sim=1.00; year=2023; nauth=14 [group G]205touvron2023llama2,arXiv:2307.09288,arxiv,Llama 2: Open Foundation and Fine-Tuned Chat Models,exact,sim=1.00; year=2023; nauth=68 [group G]206dubey2024llama3,arXiv:2407.21783,arxiv,The Llama 3 Herd of Models,exact,sim=1.00; year=2024; nauth=561 [group G]207ho2020ddpm,arXiv:2006.11239,arxiv,Denoising Diffusion Probabilistic Models,exact,sim=1.00; year=2020; nauth=3 [group G]208rombach2022ldm,10.1109/CVPR52688.2022.01042,crossref,High-Resolution Image Synthesis with Latent Diffusion Models,exact,sim=1.00; year=2022; nauth=5 [group G]209radford2021clip,arXiv:2103.00020,arxiv,Learning Transferable Visual Models From Natural Language Supervision,exact,sim=1.00; year=2021; nauth=12 [group G]210dosovitskiy2021vit,arXiv:2010.11929,arxiv,An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale,exact,sim=1.00; year=2020; nauth=12 [group G]211mildenhall2020nerf,10.1007/978-3-030-58452-8_24,crossref,NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis,exact,sim=1.00; year=2020; nauth=6 [group G]212christiano2017rlhf,arXiv:1706.03741,arxiv,Deep reinforcement learning from human preferences,exact,sim=1.00; year=2017; nauth=6 [group G]213wei2022cot,arXiv:2201.11903,arxiv,Chain-of-Thought Prompting Elicits Reasoning in Large Language Models,exact,sim=1.00; year=2022; nauth=9 [group G]214lewis2020rag,arXiv:2005.11401,arxiv,Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks,exact,sim=1.00; year=2020; nauth=12 [group G]215yao2023react,arXiv:2210.03629,arxiv,ReAct: Synergizing Reasoning and Acting in Language Models,exact,sim=1.00; year=2022; nauth=7 [group G]216schick2023toolformer,arXiv:2302.04761,arxiv,Toolformer: Language Models Can Teach Themselves to Use Tools,exact,sim=1.00; year=2023; nauth=8 [group G]217gu2023mamba,arXiv:2312.00752,arxiv,Mamba: Linear-Time Sequence Modeling with Selective State Spaces,exact,sim=1.00; year=2023; nauth=2 [group G]218fedus2022switch,arXiv:2101.03961,arxiv,Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity,exact,sim=1.00; year=2021; nauth=3 [group G]219shazeer2017moe,arXiv:1701.06538,arxiv,Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer,exact,sim=1.00; year=2017; nauth=7 [group G]220deepseek2025r1,arXiv:2501.12948,arxiv,DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning,exact,sim=1.00; year=2025; nauth=200 [group G]221deepseek2025r1nature,10.1038/s41586-025-09422-z,crossref,DeepSeek-R1 incentivizes reasoning in LLMs through reinforcement learning,exact,sim=1.00; year=2025; nauth=194 [group G]222openai2024o1,arXiv:2412.16720,arxiv,OpenAI o1 System Card,exact,sim=1.00; year=2024; nauth=265 [group G]223jumper2021alphafold,10.1038/s41586-021-03819-2,crossref,Highly accurate protein structure prediction with AlphaFold,exact,sim=1.00; year=2021; nauth=34 [group G]224silver2016alphago,10.1038/nature16961,crossref,Mastering the game of Go with deep neural networks and tree search,exact,sim=1.00; year=2016; nauth=20 [group G]225silver2017alphagozero,10.1038/nature24270,crossref,Mastering the game of Go without human knowledge,exact,sim=1.00; year=2017; nauth=17 [group G]226silver2018alphazero,10.1126/science.aar6404,crossref,"A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play",exact,sim=1.00; year=2018; nauth=13 [group G]227bommasani2021foundation,arXiv:2108.07258,arxiv,On the Opportunities and Risks of Foundation Models,exact,sim=1.00; year=2021; nauth=114 [group G]228bubeck2023sparks,arXiv:2303.12712,arxiv,Sparks of Artificial General Intelligence: Early experiments with GPT-4,exact,sim=1.00; year=2023; nauth=14 [group G]229bender2021parrots,10.1145/3442188.3445922,crossref,On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜,exact,sim=1.00; year=2021; nauth=4 [group G]230maslej2024aiindex,arXiv:2405.19522,arxiv,Artificial Intelligence Index Report 2024,exact,sim=1.00; year=2024; nauth=14 [group G]231maslej2025aiindex,arXiv:2504.07139,arxiv,Artificial Intelligence Index Report 2025,exact,sim=1.00; year=2025; nauth=23 [group G]232sevilla2022compute,10.1109/IJCNN55064.2022.9891914,crossref,Compute Trends Across Three Eras of Machine Learning,exact,sim=1.00; year=2022; nauth=6 [group G]233raffel2020t5,arXiv:1910.10683,arxiv,Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer,exact,sim=1.00; year=2019; nauth=9 [group G]234chowdhery2022palm,arXiv:2204.02311,arxiv,PaLM: Scaling Language Modeling with Pathways,exact,sim=1.00; year=2022; nauth=67 [group G]235wei2022emergent,arXiv:2206.07682,arxiv,Emergent Abilities of Large Language Models,exact,sim=1.00; year=2022; nauth=16 [group G]236schaeffer2023mirage,arXiv:2304.15004,arxiv,Are Emergent Abilities of Large Language Models a Mirage?,exact,sim=1.00; year=2023; nauth=3 [group G]237rafailov2023dpo,arXiv:2305.18290,arxiv,Direct Preference Optimization: Your Language Model is Secretly a Reward Model,exact,sim=1.00; year=2023; nauth=6 [group G]238bai2022constitutional,arXiv:2212.08073,arxiv,Constitutional AI: Harmlessness from AI Feedback,exact,sim=1.00; year=2022; nauth=51 [group G]239kojima2022zeroshotcot,arXiv:2205.11916,arxiv,Large Language Models are Zero-Shot Reasoners,exact,sim=1.00; year=2022; nauth=5 [group G]240dao2022flashattention,arXiv:2205.14135,arxiv,FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness,exact,sim=1.00; year=2022; nauth=5 [group G]241hu2021lora,arXiv:2106.09685,arxiv,LoRA: Low-Rank Adaptation of Large Language Models,exact,sim=1.00; year=2021; nauth=8 [group G]242dettmers2023qlora,arXiv:2305.14314,arxiv,QLoRA: Efficient Finetuning of Quantized LLMs,exact,sim=1.00; year=2023; nauth=4 [group G]243gemini2023,arXiv:2312.11805,arxiv,Gemini: A Family of Highly Capable Multimodal Models,exact,sim=1.00; year=2023; nauth=1351 [group G]244chen2021codex,arXiv:2107.03374,arxiv,Evaluating Large Language Models Trained on Code,exact,sim=1.00; year=2021; nauth=58 [group G]245li2022alphacode,10.1126/science.abq1158,crossref,Competition-level code generation with AlphaCode,exact,sim=1.00; year=2022; nauth=26 [group G]246kirillov2023sam,arXiv:2304.02643,arxiv,Segment Anything,exact,sim=1.00; year=2023; nauth=12 [group G]247lecun2015deeplearning,10.1038/nature14539,crossref,Deep learning,exact,sim=1.00; year=2015; nauth=3 [group G]248krizhevsky2017alexnet,10.1145/3065386,crossref,ImageNet classification with deep convolutional neural networks,exact,sim=1.00; year=2017; nauth=3 [group G]249he2016resnet,10.1109/CVPR.2016.90,crossref,Deep Residual Learning for Image Recognition,exact,sim=1.00; year=2016; nauth=4 [group G]250hochreiter1997lstm,10.1162/neco.1997.9.8.1735,crossref,Long Short-Term Memory,exact,sim=1.00; year=1997; nauth=2 [group G]251goodfellow2014gan,arXiv:1406.2661,arxiv,Generative Adversarial Networks,exact,sim=1.00; year=2014; nauth=8 [group G]252kingma2013vae,arXiv:1312.6114,arxiv,Auto-Encoding Variational Bayes,exact,sim=1.00; year=2013; nauth=2 [group G]253mnih2015dqn,10.1038/nature14236,crossref,Human-level control through deep reinforcement learning,exact,sim=1.00; year=2015; nauth=19 [group G]254schulman2017ppo,arXiv:1707.06347,arxiv,Proximal Policy Optimization Algorithms,exact,sim=1.00; year=2017; nauth=5 [group G]255turing1950computing,10.1093/mind/LIX.236.433,crossref,I.—COMPUTING MACHINERY AND INTELLIGENCE,exact,sim=0.97; year=1950; nauth=1 [group G]256mccarthy2006dartmouth,-,,,FAILED,crossref search: no title match >=0.85 [group G]257sutton2019bitterlesson,http://www.incompleteideas.net/IncIdeas/BitterLesson.html,url,The Bitter Lesson,exact,HTTP 200; page title similarity 1.00 [group G]258rumelhart1986backprop,10.1038/323533a0,crossref,Learning representations by back-propagating errors,exact,sim=1.00; year=1986; nauth=3 [group G]259bahdanau2014attention,arXiv:1409.0473,arxiv,Neural Machine Translation by Jointly Learning to Align and Translate,exact,sim=1.00; year=2014; nauth=3 [group G]260sutskever2014seq2seq,arXiv:1409.3215,arxiv,Sequence to Sequence Learning with Neural Networks,exact,sim=1.00; year=2014; nauth=3 [group G]261hendrycks2021mmlu,arXiv:2009.03300,arxiv,Measuring Massive Multitask Language Understanding,exact,sim=1.00; year=2020; nauth=7 [group G]262wang2018glue,arXiv:1804.07461,arxiv,GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding,exact,sim=1.00; year=2018; nauth=6 [group G]263liang2022helm,arXiv:2211.09110,arxiv,Holistic Evaluation of Language Models,exact,sim=1.00; year=2022; nauth=50 [group G]264chiang2024arena,arXiv:2403.04132,arxiv,Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference,exact,sim=1.00; year=2024; nauth=11 [group G]265srivastava2022bigbench,arXiv:2206.04615,arxiv,Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models,exact,sim=1.00; year=2022; nauth=451 [group G]266zheng2023judge,arXiv:2306.05685,arxiv,Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena,exact,sim=1.00; year=2023; nauth=13 [group G]267radford2019gpt2,https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf,url,"PDF first page: Language Models are Unsupervised Multitask Learners Alec Radford * 1 Jeffrey Wu * 1 Rewon Child 1 David Luan 1 Dario Amodei ** 1 Ilya Sutskever ** 1 Abstract Natural language processing tasks, such as question answering, machine translation, reading comprehension, and summarization, are typically approached with supervised learning on taskspecific datasets. We demonstrate that language models begi",exact,HTTP 200; title found in PDF first page [group G]268liang2024mapping,arXiv:2404.01268,arxiv,Mapping the Increasing Use of LLMs in Scientific Papers,exact,sim=1.00; year=2024; nauth=14 [group H]269liang2024monitoring,arXiv:2403.07183,arxiv,Monitoring AI-Modified Content at Scale: A Case Study on the Impact of ChatGPT on AI Conference Peer Reviews,exact,sim=1.00; year=2024; nauth=12 [group H]270liang2024feedback,10.1056/AIoa2400196,crossref,Can Large Language Models Provide Useful Feedback on Research Papers? A Large-Scale Empirical Analysis,exact,sim=1.00; year=2024; nauth=12 [group H]271kobak2025delving,10.1126/sciadv.adt3813,crossref,Delving into LLM-assisted writing in biomedical publications through excess vocabulary,exact,sim=1.00; year=2025; nauth=4 [group H]272geng2024writingstyle,arXiv:2404.08627,arxiv,Is ChatGPT Transforming Academics' Writing Style?,exact,sim=1.00; year=2024; nauth=2 [group H]273gray2024contamination,arXiv:2403.16887,arxiv,"ChatGPT ""contamination"": estimating the prevalence of LLMs in the scholarly literature",exact,sim=1.00; year=2024; nauth=1 [group H]274dwivedi2023sowhat,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",exact,sim=1.00; year=2023; nauth=73 [group H]275birhane2023science,10.1038/s42254-023-00581-4,crossref,Science in the age of large language models,exact,sim=1.00; year=2023; nauth=4 [group H]276messeri2024illusions,10.1038/s41586-024-07146-0,crossref,Artificial intelligence and illusions of understanding in scientific research,exact,sim=1.00; year=2024; nauth=2 [group H]277vannoorden2023survey,10.1038/d41586-023-02980-0,crossref,"AI and science: what 1,600 researchers think",exact,sim=1.00; year=2023; nauth=2 [group H]278krenn2023forecasting,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,sim=1.00; year=2023; nauth=16 [group H]279fortunato2018scienceofscience,10.1126/science.aao0185,crossref,Science of science,exact,sim=1.00; year=2018; nauth=14 [group H]280wang2021scienceofscience,10.1017/9781108610834,crossref,The Science of Science,exact,sim=1.00; year=2021; nauth=2 [group H]281lund2023chatgpt,10.1002/asi.24750,crossref,ChatGPT and a new academic reality: Artificial Intelligence‐written research papers and the ethics of the large language models in scholarly publishing,exact,sim=1.00; year=2023; nauth=6 [group H]282stokelwalker2023author,10.1038/d41586-023-00107-z,crossref,ChatGPT listed as author on research papers: many scientists disapprove,exact,sim=1.00; year=2023; nauth=1 [group H]283thorp2023notauthor,10.1126/science.adg7879,crossref,"ChatGPT is fun, but not an author",exact,sim=1.00; year=2023; nauth=1 [group H]284nature2023groundrules,10.1038/d41586-023-00191-1,crossref,Tools such as ChatGPT threaten transparent science; here are our ground rules for their use,exact,sim=1.00; year=2023; nauth=0 [group H]285else2023abstracts,10.1038/d41586-023-00056-7,crossref,Abstracts written by ChatGPT fool scientists,exact,sim=1.00; year=2023; nauth=1 [group H]286gao2023abstracts,10.1038/s41746-023-00819-6,crossref,Comparing scientific abstracts generated by ChatGPT to real abstracts with detectors and blinded human reviewers,exact,sim=1.00; year=2023; nauth=7 [group H]287noy2023productivity,10.1126/science.adh2586,crossref,Experimental evidence on the productivity effects of generative artificial intelligence,exact,sim=1.00; year=2023; nauth=2 [group H]288brynjolfsson2023genaiwork,arXiv:2304.11771,arxiv,Generative AI at Work,exact,sim=1.00; year=2023; nauth=3 [group H]289peng2023copilot,arXiv:2302.06590,arxiv,The Impact of AI on Developer Productivity: Evidence from GitHub Copilot,exact,sim=1.00; year=2023; nauth=4 [group H]290dellacqua2023jagged,10.2139/ssrn.4573321,crossref,Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of Artificial Intelligence on Knowledge Worker Productivity and Quality,minor,sim=0.93; year=2023; nauth=9 [group H]291eloundou2024gpts,10.1126/science.adj0998,crossref,GPTs are GPTs: Labor market impact potential of LLMs,exact,sim=1.00; year=2024; nauth=4 [group H]292bianchini2022aiscience,10.1016/j.respol.2022.104604,crossref,Artificial intelligence in science: An emerging general method of invention,exact,sim=1.00; year=2022; nauth=3 [group H]293gao2024aibenefits,10.1038/s41562-024-02020-5,crossref,Quantifying the use and potential benefits of artificial intelligence in scientific research,exact,sim=1.00; year=2024; nauth=2 [group H]294hao2024aiexpands,arXiv:2412.07727,arxiv,"Artificial Intelligence Tools Expand Scientists' Impact but Contract Science's Focus (Just accepted by Nature, to be online soon)",exact,sim=1.00; year=2024; nauth=4 [group H]295cockburn2018aiinnovation,10.3386/w24449,crossref,The Impact of Artificial Intelligence on Innovation,exact,sim=1.00; year=2018; nauth=3 [group H]296frank2019citationgraphs,10.1038/s42256-019-0024-5,crossref,The evolution of citation graphs in artificial intelligence research,exact,sim=1.00; year=2019; nauth=4 [group H]297klinger2020narrowing,arXiv:2009.10385,arxiv,A narrowing of AI research?,exact,sim=1.00; year=2020; nauth=3 [group H]298ahmed2020dedemocratization,arXiv:2010.15581,arxiv,The De-democratization of AI: Deep Learning and the Compute Divide in Artificial Intelligence Research,exact,sim=1.00; year=2020; nauth=2 [group H]299ahmed2023industry,10.1126/science.ade2420,crossref,The growing influence of industry in AI research,exact,sim=1.00; year=2023; nauth=3 [group H]300martinezplumed2021benchmarks,10.1038/s42256-021-00339-6,crossref,Research community dynamics behind popular AI benchmarks,exact,sim=1.00; year=2021; nauth=4 [group H]301thompson2020computationallimits,arXiv:2007.05558,arxiv,The Computational Limits of Deep Learning,exact,sim=1.00; year=2020; nauth=4 [group H]302latona2024reviewlottery,arXiv:2405.02150,arxiv,The AI Review Lottery: Widespread AI-Assisted Peer Reviews Boost Paper Scores and Acceptance Rates,exact,sim=1.00; year=2024; nauth=5 [group H]303checco2021aipeerreview,10.1057/s41599-020-00703-8,crossref,AI-assisted peer review,exact,sim=1.00; year=2021; nauth=5 [group H]304conroy2023paperfromscratch,10.1038/d41586-023-02218-z,crossref,Scientists used ChatGPT to generate an entire paper from scratch — but is it any good?,exact,sim=1.00; year=2023; nauth=1 [group H]305si2024novelideas,arXiv:2409.04109,arxiv,Can LLMs Generate Novel Research Ideas? A Large-Scale Human Study with 100+ NLP Researchers,exact,sim=1.00; year=2024; nauth=3 [group H]306lu2024aiscientist,arXiv:2408.06292,arxiv,The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery,exact,sim=1.00; year=2024; nauth=6 [group H]307boiko2023coscientist,10.1038/s41586-023-06792-0,crossref,Autonomous chemical research with large language models,exact,sim=1.00; year=2023; nauth=4 [group H]308gottweis2025coscientist,arXiv:2502.18864,arxiv,Accelerating scientific discovery with Co-Scientist,exact,sim=1.00; year=2025; nauth=51 [group H]309romeraparedes2024funsearch,10.1038/s41586-023-06924-6,crossref,Mathematical discoveries from program search with large language models,exact,sim=1.00; year=2024; nauth=12 [group H]310trinh2024alphageometry,10.1038/s41586-023-06747-5,crossref,Solving olympiad geometry without human demonstrations,exact,sim=1.00; year=2024; nauth=5 [group H]311merchant2023gnome,10.1038/s41586-023-06735-9,crossref,Scaling deep learning for materials discovery,exact,sim=1.00; year=2023; nauth=6 [group H]312gundersen2018reproducibility,10.1609/aaai.v32i1.11503,crossref,State of the Art: Reproducibility in Artificial Intelligence,exact,sim=1.00; year=2018; nauth=2 [group I]313pineau2021reproducibility,arXiv:2003.12206,arxiv,Improving Reproducibility in Machine Learning Research (A Report from the NeurIPS 2019 Reproducibility Program),exact,sim=1.00; year=2020; nauth=8 [group I]314kapoor2023leakage,10.1016/j.patter.2023.100804,crossref,Leakage and the reproducibility crisis in machine-learning-based science,exact,sim=1.00; year=2023; nauth=2 [group I]315kapoor2024reforms,10.1126/sciadv.adk3452,crossref,REFORMS: Consensus-based Recommendations for Machine-learning-based Science,exact,sim=1.00; year=2024; nauth=19 [group I]316ott2022benchmarks,10.1038/s41467-022-34591-0,crossref,Mapping global dynamics of benchmark creation and saturation in artificial intelligence,exact,sim=1.00; year=2022; nauth=5 [group I]317dehghani2021lottery,arXiv:2107.07002,arxiv,The Benchmark Lottery,exact,sim=1.00; year=2021; nauth=8 [group I]318raji2021everything,arXiv:2111.15366,arxiv,AI and the Everything in the Whole Wide World Benchmark,exact,sim=1.00; year=2021; nauth=5 [group I]319sculley2018winnerscurse,https://openreview.net/forum?id=rJWF0Fywf,url,,FAILED,HTTP 403 [group I]320acm2020badging,https://www.acm.org/publications/policies/artifact-review-and-badging-current,url,Artifact Review and Badging - Current,exact,HTTP 200; page title similarity 1.00 [group I]321wolf2020transformers,10.18653/v1/2020.emnlp-demos.6,crossref,Transformers: State-of-the-Art Natural Language Processing,exact,sim=1.00; year=2020; nauth=22 [group I]322lhoest2021datasets,10.18653/v1/2021.emnlp-demo.21,crossref,Datasets: A Community Library for Natural Language Processing,exact,sim=1.00; year=2021; nauth=32 [group I]323osborne2024huggingface,10.1007/s42001-024-00300-8,crossref,The AI community building the future? A quantitative analysis of development activity on Hugging Face Hub,exact,sim=1.00; year=2024; nauth=3 [group I]324castano2023hfcarbon,10.1109/esem56168.2023.10304801,crossref,Exploring the Carbon Footprint of Hugging Face's ML Models: A Repository Mining Study,exact,sim=1.00; year=2023; nauth=4 [group I]325hutson2018crisis,10.1126/science.359.6377.725,crossref,Artificial intelligence faces reproducibility crisis,exact,sim=1.00; year=2018; nauth=1 [group I]326henderson2018drlmatters,10.1609/aaai.v32i1.11694,crossref,Deep Reinforcement Learning That Matters,exact,sim=1.00; year=2018; nauth=6 [group I]327gebru2021datasheets,10.1145/3458723,crossref,Datasheets for datasets,exact,sim=1.00; year=2021; nauth=7 [group I]328mitchell2019modelcards,10.1145/3287560.3287596,crossref,Model Cards for Model Reporting,exact,sim=1.00; year=2019; nauth=9 [group I]329bender2018datastatements,10.1162/tacl_a_00041,crossref,Data Statements for Natural Language Processing: Toward Mitigating System Bias and Enabling Better Science,exact,sim=1.00; year=2018; nauth=2 [group I]330paullada2021discontents,10.1016/j.patter.2021.100336,crossref,Data and its (dis)contents: A survey of dataset development and use in machine learning research,exact,sim=1.00; year=2021; nauth=5 [group I]331koch2021datasetlife,arXiv:2112.01716,arxiv,"Reduced, Reused and Recycled: The Life of a Dataset in Machine Learning Research",exact,sim=1.00; year=2021; nauth=4 [group I]332baker2016reproducibility,10.1038/533452a,crossref,"1,500 scientists lift the lid on reproducibility",exact,sim=1.00; year=2016; nauth=1 [group I]333collberg2016repeatability,10.1145/2812803,crossref,Repeatability in computer systems research,exact,sim=1.00; year=2016; nauth=2 [group I]334stodden2018journalpolicy,10.1073/pnas.1708290115,crossref,An empirical analysis of journal policy effectiveness for computational reproducibility,exact,sim=1.00; year=2018; nauth=3 [group I]335nosek2015top,10.1126/science.aab2374,crossref,Promoting an open research culture,exact,sim=1.00; year=2015; nauth=39 [group I]336munafo2017manifesto,10.1038/s41562-016-0021,crossref,A manifesto for reproducible science,exact,sim=1.00; year=2017; nauth=10 [group I]337dodge2019showyourwork,10.18653/v1/D19-1224,crossref,Show Your Work: Improved Reporting of Experimental Results,exact,sim=1.00; year=2019; nauth=5 [group I]338lipton2019troubling,10.1145/3317287.3328534,crossref,Troubling Trends in Machine Learning Scholarship: Some ML papers suffer from flaws that could mislead the public and stymie future research.,exact,sim=1.00; year=2019; nauth=2 [group I]339sambasivan2021datawork,10.1145/3411764.3445518,crossref,"“Everyone wants to do the model work, not the data work”: Data Cascades in High-Stakes AI",exact,sim=1.00; year=2021; nauth=6 [group I]340kiela2021dynabench,10.18653/v1/2021.naacl-main.324,crossref,Dynabench: Rethinking Benchmarking in NLP,exact,sim=1.00; year=2021; nauth=19 [group I]341sainz2023contamination,10.18653/v1/2023.findings-emnlp.722,crossref,NLP Evaluation in trouble: On the Need to Measure LLM Data Contamination for each Benchmark,exact,sim=1.00; year=2023; nauth=6 [group I]342recht2019imagenet,arXiv:1902.10811,arxiv,Do ImageNet Classifiers Generalize to ImageNet?,exact,sim=1.00; year=2019; nauth=4 [group I]343bommasani2023fmti,arXiv:2310.12941,arxiv,The Foundation Model Transparency Index,exact,sim=1.00; year=2023; nauth=8 [group I]344longpre2024dataprovenance,10.1038/s42256-024-00878-8,crossref,A large-scale audit of dataset licensing and attribution in AI,exact,sim=1.00; year=2024; nauth=17 [group I]345semmelrock2025reproducibility,10.1002/aaai.70002,crossref,"Reproducibility in machine‐learning‐based research: Overview, barriers, and drivers",exact,sim=1.00; year=2025; nauth=7 [group I]346gundersen2022sources,10.48550/arxiv.2204.07610,openalex,Sources of Irreproducibility in Machine Learning: A Review,exact,sim=1.00; year=2022; nauth=4 [group I]347wilkinson2016fair,10.1038/sdata.2016.18,crossref,The FAIR Guiding Principles for scientific data management and stewardship,exact,sim=1.00; year=2016; nauth=53 [group I]348magnusson2023olmo,arXiv:2402.00838,arxiv,OLMo: Accelerating the Science of Language Models,exact,sim=1.00; year=2024; nauth=43 [group I]349preskill2018nisq,10.22331/q-2018-08-06-79,crossref,Quantum Computing in the NISQ era and beyond,exact,sim=1.00; year=2018; nauth=1 [group J]350arute2019supremacy,10.1038/s41586-019-1666-5,crossref,Quantum supremacy using a programmable superconducting processor,exact,sim=1.00; year=2019; nauth=77 [group J]351kim2023utility,10.1038/s41586-023-06096-3,crossref,Evidence for the utility of quantum computing before fault tolerance,exact,sim=1.00; year=2023; nauth=11 [group J]352googlequantum2025belowthreshold,10.1038/s41586-024-08449-y,crossref,Quantum error correction below the surface code threshold,exact,sim=1.00; year=2025; nauth=256 [group J]353cerezo2021vqa,10.1038/s42254-021-00348-9,crossref,Variational quantum algorithms,exact,sim=1.00; year=2021; nauth=11 [group J]354bharti2022nisq,10.1103/RevModPhys.94.015004,crossref,Noisy intermediate-scale quantum algorithms,exact,sim=1.00; year=2022; nauth=14 [group J]355shor1997factoring,10.1137/S0097539795293172,crossref,Polynomial-Time Algorithms for Prime Factorization and Discrete Logarithms on a Quantum Computer,exact,sim=1.00; year=1997; nauth=1 [group J]356nakamoto2008bitcoin,https://bitcoin.org/bitcoin.pdf,url,"PDF first page: Bitcoin: A Peer-to-Peer Electronic Cash System Satoshi Nakamoto satoshin@gmx.com www.bitcoin.org Abstract. A purely peer-to-peer version of electronic cash would allow online payments to be sent directly from one party to another without going through a financial institution. Digital signatures provide part of the solution, but the main benefits are lost if a trusted third party is still required ",exact,HTTP 200; title found in PDF first page [group J]357zheng2018blockchain,10.1504/IJWGS.2018.095647,crossref,Blockchain challenges and opportunities: a survey,exact,sim=1.00; year=2018; nauth=5 [group J]358monrat2019blockchain,10.1109/ACCESS.2019.2936094,crossref,"A Survey of Blockchain From the Perspectives of Applications, Challenges, and Opportunities",exact,sim=1.00; year=2019; nauth=3 [group J]359werner2022defi,10.1145/3558535.3559780,crossref,SoK: Decentralized Finance (DeFi),exact,sim=1.00; year=2022; nauth=6 [group J]360mcmahan2017fl,arXiv:1602.05629,arxiv,Communication-Efficient Learning of Deep Networks from Decentralized Data,exact,sim=1.00; year=2016; nauth=5 [group J]361kairouz2021fl,10.1561/2200000083,crossref,Advances and Open Problems in Federated Learning,exact,sim=1.00; year=2021; nauth=2 [group J]362li2020flchallenges,10.1109/MSP.2020.2975749,crossref,"Federated Learning: Challenges, Methods, and Future Directions",exact,sim=1.00; 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year=2023; nauth=4 [group J]379hennessy2019goldenage,10.1145/3282307,crossref,A new golden age for computer architecture,exact,sim=1.00; year=2019; nauth=2 [group J]380leiserson2020roomattop,10.1126/science.aam9744,crossref,There’s plenty of room at the Top: What will drive computer performance after Moore’s law?,exact,sim=1.00; year=2020; nauth=7 [group J]381jouppi2017tpu,10.1145/3079856.3080246,crossref,In-Datacenter Performance Analysis of a Tensor Processing Unit,exact,sim=1.00; year=2017; nauth=76 [group J]382jouppi2023tpuv4,10.1145/3579371.3589350,crossref,TPU v4: An Optically Reconfigurable Supercomputer for Machine Learning with Hardware Support for Embeddings,exact,sim=1.00; year=2023; nauth=14 [group J]383reuther2022accelerators,10.1109/HPEC55821.2022.9926331,crossref,AI and ML Accelerator Survey and Trends,exact,sim=1.00; year=2022; nauth=6 [group J]384sze2017efficientdnn,10.1109/JPROC.2017.2761740,crossref,Efficient Processing of Deep Neural Networks: A Tutorial and Survey,exact,sim=1.00; year=2017; nauth=4 [group J]385dally2021gpu,10.1109/MM.2021.3113475,crossref,Evolution of the Graphics Processing Unit (GPU),exact,sim=1.00; year=2021; nauth=3 [group J]386gholami2024memorywall,10.1109/MM.2024.3373763,crossref,AI and Memory Wall,exact,sim=1.00; year=2024; nauth=6 [group J]387dennard1974scaling,10.1109/JSSC.1974.1050511,crossref,Design of ion-implanted MOSFET's with very small physical dimensions,exact,sim=1.00; year=1974; nauth=6 [group J]388patterson2021carbon,arXiv:2104.10350,arxiv,Carbon Emissions and Large Neural Network Training,exact,sim=1.00; year=2021; nauth=9 [group J]389patterson2022plateau,10.1109/MC.2022.3148714,crossref,"The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrink",exact,sim=1.00; year=2022; nauth=10 [group J]390strubell2019energy,10.18653/v1/P19-1355,crossref,Energy and Policy Considerations for Deep Learning in NLP,exact,sim=1.00; year=2019; nauth=3 [group J]391luccioni2023bloom,arXiv:2211.02001,arxiv,"Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Model",exact,sim=1.00; 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year=2022; nauth=5 [group J]551esser2024sd3,arXiv:2403.03206,arxiv,Scaling Rectified Flow Transformers for High-Resolution Image Synthesis,exact,sim=1.00; year=2024; nauth=17 [group J]552peebles2023dit,10.1109/ICCV51070.2023.00387,crossref,Scalable Diffusion Models with Transformers,exact,sim=1.00; year=2023; nauth=2 [group J]553karras2019stylegan,10.1109/CVPR.2019.00453,crossref,A Style-Based Generator Architecture for Generative Adversarial Networks,exact,sim=1.00; year=2019; nauth=3 [group J]554brooks2024sora,https://openai.com/index/video-generation-models-as-world-simulators/,url,,FAILED,HTTP 200; page title similarity 0.00 [group J]555shneiderman2020hcai,10.1080/10447318.2020.1741118,crossref,"Human-Centered Artificial Intelligence: Reliable, Safe & Trustworthy",exact,sim=1.00; year=2020; nauth=1 [group J]556amershi2019guidelines,10.1145/3290605.3300233,crossref,Guidelines for Human-AI Interaction,exact,sim=1.00; year=2019; nauth=13 [group J]557weisz2024genaidesign,10.1145/3613904.3642466,crossref,Design Principles for Generative AI Applications,exact,sim=1.00; 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