1% ============================================================================2% Appendix A — The ontology of computer-science research (taxonomy)3% Written by Agent L (appendices A/E/H instance), 2026-11-03.4% Sources of truth: taxonomy/ONTOLOGY.md (hand-written §1–§3 and generated tables), taxonomy/ontology.yaml (v1.0,5% 2026-11-03), taxonomy/topic_map.csv, taxonomy/classify.py, taxonomy/VALIDATION.md. Counts in this appendix are6% properties of the ontology (numbers of signals, mapped topics and venues), computed from ontology.yaml; they are7% not corpus results. Validation results are reported in Part IX.8% ============================================================================910\section{The Ontology of Computer-Science Research}11\label{app:taxonomy}1213This appendix documents the classification scheme that assigns every eligible work of the corpus to one or14more of the forty research categories listed in the mandate (Section~\ref{sec:m-classification} describes the15scoring rule and its validation design). The scheme is an \emph{extensible hierarchical ontology} rather than a16flat keyword list: forty leaf categories are grouped into eight areas, a small set of parent--child edges records17where a leaf specialises another leaf, and a denser set of ``related'' edges records thematic adjacency. The18mapping from bibliographic signals to leaves is data, not prose. It lives in one Python module per area19(\texttt{taxonomy/src/*.py}), is validated and serialised by \texttt{taxonomy/build\_ontology.py} into the20machine-readable file \texttt{taxonomy/ontology.yaml} (version~1.0, built on 2026-11-03 from the OpenAlex topic21export of 2026-10-14 and the DBLP stream table of 2018--2026) and into an audit table of OpenAlex topics22(\texttt{taxonomy/topic\_map.csv}). Everything stated below is derived from those files; the tables were23regenerated from the YAML file rather than transcribed, and the counts they contain are properties of the24ontology itself (how many topics, venues and patterns point to a category), not statistics of the corpus. The25agreement of the rules with independent raters on the stratified validation sample is reported in Part~IX and is26deliberately not anticipated here.2728\subsection{Structure: areas, leaves and edges}29\label{app:taxonomy-structure}3031The ontology has three levels. The eight areas of Table~\ref{tab:appA-areas} are an organisational device for the32manuscript (they correspond to the disciplinary reviews of Parts~V--VII and to the area-level flow statistics of33Part~VIII); classification operates on leaves only, and every leaf belongs to exactly one area. The forty leaves34are numbered as in the mandate (Table~\ref{tab:appA-leaves}). Seven \texttt{parent\_\allowbreak of}/\texttt{child\_\allowbreak of}35edges record genuine specialisation:36\texttt{foundation\_\allowbreak models} $\rightarrow$ \texttt{llm} and \texttt{multimodal\_\allowbreak ai};37\texttt{programming\_\allowbreak languages} $\rightarrow$ \texttt{compilers};38\texttt{distributed\_\allowbreak computing} $\rightarrow$ \texttt{cloud\_\allowbreak computing} and \texttt{edge\_\allowbreak computing};39\texttt{computer\_\allowbreak architecture} $\rightarrow$ \texttt{ai\_\allowbreak accelerators};40\texttt{privacy\_\allowbreak preserving\_\allowbreak computation} $\rightarrow$ \texttt{federated\_\allowbreak learning}.41These edges have one operational consequence, the tie-breaking rule for the primary category (a child wins42over its parent at equal score), and one interpretive consequence: a work carrying both labels is not43double-counted as evidence of convergence between distinct fields, since the two labels are nested by44construction. The \texttt{related} edges (236 directed entries in the YAML file, 144 distinct unordered pairs)45carry no weight in classification; they document which categories the ontology's author expected to share46works, and they are used in Part~IX only to separate expected from unexpected co-classification.4748\begin{table}[htbp]49\begin{singlespace}50\centering51\caption{The eight areas and their leaves (\texttt{ontology.yaml}, \texttt{areas}).}52\label{tab:appA-areas}53\small54\begin{tabularx}{\textwidth}{p{3.3cm} p{3.9cm} c X}55\toprule56Area id & Name & Leaves & Leaf identifiers \\57\midrule58\texttt{ai\_\allowbreak ml} & Artificial Intelligence and Machine Learning & 9 &59 \texttt{llm}, \texttt{foundation\_\allowbreak models}, \texttt{multimodal\_\allowbreak ai}, \texttt{generative\_\allowbreak ai}, \texttt{ai\_\allowbreak agents},60 \texttt{reinforcement\_\allowbreak learning}, \texttt{ml\_\allowbreak theory}, \texttt{deep\_\allowbreak learning\_\allowbreak architectures}, \texttt{ai\_\allowbreak safety} \\61\texttt{perception\_\allowbreak interaction} & Perception, Language, Robotics and Interaction & 6 &62 \texttt{computer\_\allowbreak vision}, \texttt{nlp}, \texttt{speech\_\allowbreak audio}, \texttt{robotics}, \texttt{hci}, \texttt{computer\_\allowbreak graphics} \\63\texttt{software\_\allowbreak pl} & Software, Programming Languages and Formal Methods & 4 &64 \texttt{software\_\allowbreak engineering}, \texttt{programming\_\allowbreak languages}, \texttt{compilers}, \texttt{formal\_\allowbreak verification} \\65\texttt{theory} & Theory of Computation & 2 & \texttt{algorithms}, \texttt{computational\_\allowbreak complexity} \\66\texttt{data\_\allowbreak distributed} & Data, Information and Distributed Systems & 6 &67 \texttt{database\_\allowbreak systems}, \texttt{information\_\allowbreak retrieval}, \texttt{distributed\_\allowbreak computing}, \texttt{cloud\_\allowbreak computing},68 \texttt{edge\_\allowbreak computing}, \texttt{computer\_\allowbreak networks} \\69\texttt{security\_\allowbreak privacy} & Security, Privacy and Trust & 5 &70 \texttt{cybersecurity}, \texttt{applied\_\allowbreak cryptography}, \texttt{blockchain}, \texttt{privacy\_\allowbreak preserving\_\allowbreak computation},71 \texttt{federated\_\allowbreak learning} \\72\texttt{hardware\_\allowbreak emerging} & Hardware, Architecture and Emerging Computing & 5 &73 \texttt{computer\_\allowbreak architecture}, \texttt{ai\_\allowbreak accelerators}, \texttt{operating\_\allowbreak systems}, \texttt{quantum\_\allowbreak computing},74 \texttt{sustainable\_\allowbreak computing} \\75\texttt{computational\_\allowbreak science} & Computational Science and Discovery & 3 &76 \texttt{scientific\_\allowbreak computing}, \texttt{computational\_\allowbreak biology}, \texttt{autonomous\_\allowbreak scientific\_\allowbreak discovery} \\77\bottomrule78\end{tabularx}79\end{singlespace}80\end{table}8182Two features of the grouping deserve comment because they are choices, not facts. First, \texttt{ai\_\allowbreak safety}83is placed under the AI area rather than under security and privacy, although adversarial robustness and84backdoors are scored in both \texttt{ai\_\allowbreak safety} and \texttt{cybersecurity}; the ontology treats safety as a85property of learning systems, with security as the adjacent field. Second, \texttt{computer\_\allowbreak graphics} sits in86the perception area next to vision and HCI rather than in a visual-computing area of its own, and87\texttt{federated\_\allowbreak learning} sits under privacy rather than under distributed computing, which reflects the88definitional emphasis of the leaf (training without centralising raw data) rather than its algorithmic89kinship. Readers who would group the leaves differently can do so from the leaf-level tables of Part~IX90without re-running anything, because areas are never used in scoring.9192\subsection{The forty categories}93\label{app:taxonomy-leaves}9495Table~\ref{tab:appA-leaves} reproduces the definition of each leaf exactly as it is given to the classifier's96author and to the validation raters (the raters receive these definitions and nothing else about the rules).97Each definition states the object of the category, lists its typical sub-topics and names the neighbouring98category to which excluded work belongs; the latter clause is what makes the definitions a partition rather than99a list of keywords, and it is the part most often invoked in the ambiguity decisions of100Section~\ref{app:taxonomy-ambiguities}. Identifiers are those of the YAML file; numbers are those of the101mandate.102103\begin{singlespace}104\begin{small}105\begin{longtable}{@{}r p{3.4cm} p{10.6cm}@{}}106\caption{The forty leaf categories: identifier, area and definition (\texttt{ontology.yaml}, \texttt{categories[].definition}). Parent/child edges are given under the identifier.}107\label{tab:appA-leaves}\\108\toprule109No. & Category & Definition \\110\midrule111\endfirsthead112\caption[]{The forty leaf categories (continued).}\\113\toprule114No. & Category & Definition \\115\midrule116\endhead117\midrule118\multicolumn{3}{r}{\footnotesize continued on next page}\\119\endfoot120\bottomrule121\endlastfoot1221 & \textbf{Large Language Models}\newline {\footnotesize \texttt{llm}\newline area \texttt{ai\_\allowbreak ml}; child of \texttt{foundation\_\allowbreak models}} & Research whose object is a transformer-based language model trained at scale: pre-training, instruction tuning, prompting and in-context learning, reasoning, retrieval augmentation, efficient inference, evaluation and benchmarks of LLMs. Excludes classical NLP tasks solved with small task-specific models (nlp) and non-language foundation models (foundation\_models). \\[2pt]1232 & \textbf{Foundation Models}\newline {\footnotesize \texttt{foundation\_\allowbreak models}\newline area \texttt{ai\_\allowbreak ml}; parent of \texttt{llm}, \texttt{multimodal\_\allowbreak ai}} & Large pre-trained models intended for adaptation to many downstream tasks in any modality: scaling laws, pre-training objectives, self-supervised representation learning at scale, transfer, fine-tuning and adapter methods, model releases and their evaluation. Language-specific work is llm (a child); vision-language work is multimodal\_ai (a child). \\[2pt]1243 & \textbf{Multimodal AI}\newline {\footnotesize \texttt{multimodal\_\allowbreak ai}\newline area \texttt{ai\_\allowbreak ml}; child of \texttt{foundation\_\allowbreak models}} & Models and systems that jointly process two or more modalities (vision-language, audio-visual, video-text, 3D-language, sensors with language): vision-language models and multimodal LLMs, captioning, visual question answering, cross-modal retrieval and grounding. Unimodal vision is computer\_vision; pure speech is speech\_audio. \\[2pt]1254 & \textbf{Generative AI}\newline {\footnotesize \texttt{generative\_\allowbreak ai}\newline area \texttt{ai\_\allowbreak ml}} & Models that synthesise new content (images, video, audio, 3D, text, code, molecules) and their training, sampling, evaluation, detection and misuse: GANs, VAEs, diffusion and flow models, autoregressive generators, text-to-X synthesis, deepfakes and AI-generated-content detection. LLM text generation as such is llm unless the contribution concerns the generative model or its outputs' authenticity. \\[2pt]1265 & \textbf{AI Agents and Autonomous Systems}\newline {\footnotesize \texttt{ai\_\allowbreak agents}\newline area \texttt{ai\_\allowbreak ml}} & Software agents that perceive, decide and act with some autonomy: LLM-based agents and tool use, multi-agent systems, planning and automated reasoning for acting, agent architectures, negotiation, agentic workflows and benchmarks, embodied agents in simulated environments. Physical robots are robotics; RL algorithms as such are reinforcement\_learning. \\[2pt]1276 & \textbf{Reinforcement Learning}\newline {\footnotesize \texttt{reinforcement\_\allowbreak learning}\newline area \texttt{ai\_\allowbreak ml}} & Learning to act from reward signals: value- and policy-based methods, model-based RL, offline and inverse RL, multi-agent RL, bandits, exploration, reward design, RL from human feedback, and RL theory. Applications to robot control are shared with robotics. \\[2pt]1287 & \textbf{Machine Learning Theory}\newline {\footnotesize \texttt{ml\_\allowbreak theory}\newline area \texttt{ai\_\allowbreak ml}} & Statistical and computational learning theory and principled ML methodology: generalisation and sample complexity, optimisation theory for learning, kernel and Bayesian methods, probabilistic models, causal inference, online learning, PAC learning, interpretability and fairness as formal problems. Architecture engineering is deep\_learning\_architectures. \\[2pt]1298 & \textbf{Deep Learning Architectures}\newline {\footnotesize \texttt{deep\_\allowbreak learning\_\allowbreak architectures}\newline area \texttt{ai\_\allowbreak ml}} & Design, training and compression of neural network architectures as such: CNNs, RNNs, transformers and attention, graph neural networks, state-space models, normalisation and optimisers in practice, neural architecture search, pruning, quantisation and distillation, neuromorphic and spiking networks as models. Task-specific results go to the application category; hardware goes to ai\_accelerators. \\[2pt]1309 & \textbf{Computer Vision}\newline {\footnotesize \texttt{computer\_\allowbreak vision}\newline area \texttt{perception\_\allowbreak interaction}} & Extracting information from images, video and 3D data: recognition, detection, segmentation, tracking, pose, depth and 3D reconstruction, image restoration and enhancement, medical and remote-sensing image analysis, visual representation learning. Image synthesis is generative\_ai; vision-language is multimodal\_ai; rendering is computer\_graphics. \\[2pt]13110 & \textbf{Natural Language Processing}\newline {\footnotesize \texttt{nlp}\newline area \texttt{perception\_\allowbreak interaction}} & Computational processing of human language: machine translation, parsing and tagging, information extraction, sentiment and text classification, summarisation, question answering, dialogue, text mining, computational linguistics, low-resource and multilingual NLP, NLP evaluation and resources. LLM-centred work is llm (also classified here when the task framing dominates). \\[2pt]13211 & \textbf{Speech and Audio Computing}\newline {\footnotesize \texttt{speech\_\allowbreak audio}\newline area \texttt{perception\_\allowbreak interaction}} & Processing and generation of speech, music and general audio: speech recognition and synthesis, speaker and language recognition, enhancement and separation, spoken language understanding, music information retrieval and generation, audio event detection, neural audio codecs. Text-only dialogue is nlp. \\[2pt]13312 & \textbf{Robotics}\newline {\footnotesize \texttt{robotics}\newline area \texttt{perception\_\allowbreak interaction}} & Machines that sense and act in the physical world: manipulation, locomotion, motion and path planning, SLAM and localisation, robot learning, human-robot interaction, autonomous vehicles and drones as robotic systems, soft and swarm robotics, teleoperation. RL algorithms are reinforcement\_learning; robotic process automation and surgical-outcome studies are excluded. \\[2pt]13413 & \textbf{Human-Computer Interaction}\newline {\footnotesize \texttt{hci}\newline area \texttt{perception\_\allowbreak interaction}} & Design, evaluation and understanding of interactive systems and their users: user studies and usability, interaction techniques, VR/AR/XR, accessibility and assistive technology, CSCW and social computing, human-AI interaction, ubiquitous and wearable computing, affective computing. Rendering is computer\_graphics; physical robots are robotics. \\[2pt]13514 & \textbf{Software Engineering}\newline {\footnotesize \texttt{software\_\allowbreak engineering}\newline area \texttt{software\_\allowbreak pl}} & How software is specified, built, tested, maintained and studied: requirements, architecture, testing and debugging, program repair, mining software repositories, empirical studies of developers, DevOps, model-driven engineering, open-source ecosystems, and AI-assisted software development. Language design is programming\_languages; proofs of correctness are formal\_verification. \\[2pt]13615 & \textbf{Programming Languages}\newline {\footnotesize \texttt{programming\_\allowbreak languages}\newline area \texttt{software\_\allowbreak pl}; parent of \texttt{compilers}} & Design, semantics, type systems and implementation principles of programming languages: type theory, operational and denotational semantics, functional and logic programming, effects, concurrency models, memory safety, domain-specific languages, program analysis as a language problem. Code generation and optimisation pipelines are compilers (a child); machine-checked correctness is formal\_verification. \\[2pt]13716 & \textbf{Compilers}\newline {\footnotesize \texttt{compilers}\newline area \texttt{software\_\allowbreak pl}; child of \texttt{programming\_\allowbreak languages}} & Translation and optimisation of programs for execution: compiler construction, intermediate representations, optimisation passes, code generation, JIT compilation, auto-vectorisation and parallelisation, polyhedral methods, high-level synthesis, and compilers for machine-learning workloads and accelerators. A child of programming\_languages. \\[2pt]13817 & \textbf{Formal Verification}\newline {\footnotesize \texttt{formal\_\allowbreak verification}\newline area \texttt{software\_\allowbreak pl}} & Mathematically rigorous establishment of system properties: model checking, theorem proving and proof assistants, SAT/SMT solving and automated reasoning, deductive and runtime verification, verified software and hardware, formal semantics as a verification tool, verification of neural networks, smart contracts and protocols, automata and temporal logics. Language semantics as design is programming\_languages. \\[2pt]13918 & \textbf{Algorithms and Data Structures}\newline {\footnotesize \texttt{algorithms}\newline area \texttt{theory}} & Design and analysis of algorithms and data structures: graph, string, geometric, approximation, randomized, online, streaming, parallel and parameterized algorithms, combinatorial optimisation and metaheuristics, algorithmic game theory, algorithm engineering, and the combinatorics that underpins them. Lower bounds and hardness as such are computational\_complexity; numerical algorithms are scientific\_computing. \\[2pt]14019 & \textbf{Computational Complexity}\newline {\footnotesize \texttt{computational\_\allowbreak complexity}\newline area \texttt{theory}} & The intrinsic difficulty of computational problems: complexity classes and their separations, hardness of approximation, circuit, proof, communication and query complexity, fine-grained and parameterized complexity, pseudorandomness and derandomization, computability and automata theory, algebraic complexity, and quantum complexity. Algorithm design is algorithms; cryptographic hardness assumptions are applied\_cryptography. \\[2pt]14120 & \textbf{Database Systems}\newline {\footnotesize \texttt{database\_\allowbreak systems}\newline area \texttt{data\_\allowbreak distributed}} & Storage, querying and management of structured and semi-structured data: query processing and optimisation, transactions and concurrency control, indexing and storage engines, data integration, cleaning and provenance, big-data processing frameworks, data mining as a data-management problem, learned database components, vector and graph databases. Ranking of unstructured documents is information\_retrieval. \\[2pt]14221 & \textbf{Information Retrieval}\newline {\footnotesize \texttt{information\_\allowbreak retrieval}\newline area \texttt{data\_\allowbreak distributed}} & Finding and ranking relevant information for a user need: search engines and ranking models, dense and sparse retrieval, recommender systems, query understanding, evaluation and test collections, web search and mining, scholarly and digital-library retrieval, retrieval components of RAG systems. Structured query processing is database\_systems; text understanding tasks are nlp. \\[2pt]14322 & \textbf{Distributed Computing}\newline {\footnotesize \texttt{distributed\_\allowbreak computing}\newline area \texttt{data\_\allowbreak distributed}; parent of \texttt{cloud\_\allowbreak computing}, \texttt{edge\_\allowbreak computing}} & Computation across multiple processors or machines: distributed algorithms and consensus, fault tolerance and replication, parallel programming and HPC systems software, cluster and grid resource management, distributed storage and middleware, distributed and parallel ML training systems. Cloud service models are cloud\_computing and edge placement is edge\_computing (both children). \\[2pt]14423 & \textbf{Cloud Computing}\newline {\footnotesize \texttt{cloud\_\allowbreak computing}\newline area \texttt{data\_\allowbreak distributed}; child of \texttt{distributed\_\allowbreak computing}} & Computing delivered as elastic, multi-tenant, on-demand services: IaaS/PaaS/SaaS and serverless, virtualisation and containers, resource provisioning and auto-scaling, SLAs and pricing, cloud-native architectures, data-centre resource management, cloud security and migration. A child of distributed\_computing; placement at the network edge is edge\_computing. \\[2pt]14524 & \textbf{Edge Computing}\newline {\footnotesize \texttt{edge\_\allowbreak computing}\newline area \texttt{data\_\allowbreak distributed}; child of \texttt{distributed\_\allowbreak computing}} & Computation placed near data sources and users: edge/fog/MEC architectures, computation offloading, on-device and TinyML inference, edge-cloud collaboration and split computing, IoT platforms as computing substrates, latency-critical edge services, edge caching and orchestration. Radio access and protocols are computer\_networks; model compression as such is deep\_learning\_architectures or ai\_accelerators. \\[2pt]14625 & \textbf{Computer Networks}\newline {\footnotesize \texttt{computer\_\allowbreak networks}\newline area \texttt{data\_\allowbreak distributed}} & Design, protocols, measurement and performance of communication networks: Internet architecture and transport, routing, wireless and mobile networks, 5G/6G and software-defined networking, network management, sensor and vehicular networks, content delivery and caching, network coding and information theory applied to networking. Physical-layer signal processing is included only when framed as networking. \\[2pt]14726 & \textbf{Cybersecurity}\newline {\footnotesize \texttt{cybersecurity}\newline area \texttt{security\_\allowbreak privacy}} & Protecting computing systems, networks, software and users from attack: vulnerability discovery and exploitation, malware and intrusion detection, authentication and access control, system and hardware security, web and mobile security, digital forensics, security of AI systems, security operations and human factors of security. Cryptographic primitives and protocols are applied\_cryptography; data-protection techniques are privacy\_preserving\_computation. \\[2pt]14827 & \textbf{Applied Cryptography}\newline {\footnotesize \texttt{applied\_\allowbreak cryptography}\newline area \texttt{security\_\allowbreak privacy}} & Design, analysis, implementation and deployment of cryptographic primitives and protocols: symmetric and public-key schemes, hash functions, signatures, key exchange, post-quantum cryptography, zero-knowledge proofs, secure multi-party computation and homomorphic encryption as primitives, cryptanalysis, side-channel-resistant implementations, cryptographic hardware, provable security, quantum key distribution. Deployment of these primitives for data protection is privacy\_preserving\_computation; ledgers are blockchain. \\[2pt]14928 & \textbf{Blockchain Systems}\newline {\footnotesize \texttt{blockchain}\newline area \texttt{security\_\allowbreak privacy}} & Distributed ledgers and the systems built on them: consensus for permissioned and permissionless chains, smart contracts and their security, scalability (sharding, layer-2, rollups), cryptocurrencies and DeFi mechanisms, blockchain applications and governance, measurement of blockchain ecosystems. Generic BFT consensus theory is shared with distributed\_computing; primitives are applied\_cryptography. \\[2pt]15029 & \textbf{Computer Architecture}\newline {\footnotesize \texttt{computer\_\allowbreak architecture}\newline area \texttt{hardware\_\allowbreak emerging}; parent of \texttt{ai\_\allowbreak accelerators}} & Organisation and design of processors, memory systems and digital hardware: microarchitecture, caches and memory hierarchies, interconnects and networks-on-chip, FPGAs and reconfigurable computing, embedded and real-time systems, hardware/software co-design, electronic design automation, VLSI and low-power design, processing-in-memory. Accelerators specialised for ML are ai\_accelerators (a child); kernels are operating\_systems. \\[2pt]15130 & \textbf{Accelerators and AI Hardware}\newline {\footnotesize \texttt{ai\_\allowbreak accelerators}\newline area \texttt{hardware\_\allowbreak emerging}; child of \texttt{computer\_\allowbreak architecture}} & Hardware and hardware-aware software for efficient machine-learning computation: GPU/TPU/NPU design and programming, ASIC and FPGA DNN accelerators, neuromorphic and spiking hardware, in-memory and analog/photonic computing for AI, quantisation and sparsity exploited by hardware, efficient LLM inference and serving systems, ML compilers' hardware mapping, benchmarks such as MLPerf. A child of computer\_architecture. \\[2pt]15231 & \textbf{Operating Systems}\newline {\footnotesize \texttt{operating\_\allowbreak systems}\newline area \texttt{hardware\_\allowbreak emerging}} & System software that manages hardware and provides execution abstractions: kernels and drivers, scheduling, memory management, file and storage systems, virtualisation and containers, OS security and isolation, I/O and networking stacks, real-time and embedded OS, systems support for new hardware (persistent memory, CXL, SmartNICs). Distributed coordination is distributed\_computing; hardware design is computer\_architecture. \\[2pt]15332 & \textbf{Quantum Computing}\newline {\footnotesize \texttt{quantum\_\allowbreak computing}\newline area \texttt{hardware\_\allowbreak emerging}} & Computation and information processing exploiting quantum mechanics: quantum algorithms and complexity, quantum error correction and fault tolerance, qubit hardware platforms and control, quantum software, compilers and simulators, variational and NISQ algorithms, quantum machine learning, quantum communication and key distribution, quantum networking. Quantum physics without a computational or information-processing framing is out of scope; post-quantum cryptography is applied\_cryptography. \\[2pt]15433 & \textbf{Scientific Computing}\newline {\footnotesize \texttt{scientific\_\allowbreak computing}\newline area \texttt{computational\_\allowbreak science}} & Numerical and computational methods and software for mathematical models of science and engineering: numerical linear algebra, discretisation of differential equations, solvers and preconditioners, simulation frameworks, uncertainty quantification, reduced-order and surrogate models, scientific machine learning (PINNs, neural operators), high-performance scientific software and performance portability, mathematical software, computational physics/chemistry/engineering tooling. Domain biology is computational\_biology; parallel systems software is distributed\_computing. \\[2pt]15534 & \textbf{Computational Biology}\newline {\footnotesize \texttt{computational\_\allowbreak biology}\newline area \texttt{computational\_\allowbreak science}} & Computational methods for biological and biomedical data: bioinformatics and genomics, protein structure and design, molecular modelling and computational drug discovery, single-cell and multi-omics analysis, systems and synthetic biology, biomedical text mining, computational neuroscience, biomedical and health informatics methods, DNA/molecular computing. Medical image analysis is shared with computer\_vision; numerical methods as such are scientific\_computing. \\[2pt]15635 & \textbf{Computer Graphics}\newline {\footnotesize \texttt{computer\_\allowbreak graphics}\newline area \texttt{perception\_\allowbreak interaction}} & Synthesis and manipulation of visual content from models: rendering, geometry processing and modelling, animation and simulation for graphics, neural rendering and 3D representations, visualisation, procedural content, game and VR engines. Image understanding is computer\_vision; pixel-space generative models are generative\_ai; user studies of VR are hci. \\[2pt]15736 & \textbf{Privacy-Preserving Computation}\newline {\footnotesize \texttt{privacy\_\allowbreak preserving\_\allowbreak computation}\newline area \texttt{security\_\allowbreak privacy}; parent of \texttt{federated\_\allowbreak learning}} & Computing on, releasing or sharing data while limiting what is learned about individuals: differential privacy, homomorphic encryption, secure multi-party computation and TEEs applied to data analysis and ML, anonymisation and synthetic data, private information retrieval, privacy attacks on models and their defences, machine unlearning, privacy engineering and regulation (GDPR). Federated learning is a child; cryptographic constructions as such are applied\_cryptography. \\[2pt]15837 & \textbf{Federated Learning}\newline {\footnotesize \texttt{federated\_\allowbreak learning}\newline area \texttt{security\_\allowbreak privacy}; child of \texttt{privacy\_\allowbreak preserving\_\allowbreak computation}} & Training models across multiple clients or institutions without centralising their raw data: federated averaging and optimisation under heterogeneity, communication efficiency, client selection, personalisation, secure aggregation and privacy of federated systems, robustness to malicious clients, vertical and cross-silo federated learning, federated fine-tuning of foundation models, decentralised/gossip learning. A child of privacy\_preserving\_computation; also linked to distributed\_computing and edge\_computing. \\[2pt]15938 & \textbf{AI Safety and Alignment}\newline {\footnotesize \texttt{ai\_\allowbreak safety}\newline area \texttt{ai\_\allowbreak ml}} & Making AI systems safe, aligned, robust, fair, interpretable and governable: alignment and preference learning framed as a safety problem, red-teaming and jailbreaks, adversarial robustness and backdoors, hallucination and truthfulness, bias and fairness, mechanistic interpretability, evaluation of dangerous capabilities, AI governance and regulation. Generic system security is cybersecurity; XAI as a statistical method only is ml\_theory. \\[2pt]16039 & \textbf{Sustainable Computing}\newline {\footnotesize \texttt{sustainable\_\allowbreak computing}\newline area \texttt{hardware\_\allowbreak emerging}} & The energy, carbon, water and material footprint of computing and how to reduce it: energy-efficient and carbon-aware systems, data-centre energy and cooling, Green AI and the cost of training and inference, life-cycle and embodied carbon of hardware, green software engineering and networking, energy harvesting and intermittent computing, computing for sustainability when the object studied is the computing system. Generic low-power circuit design is computer\_architecture; energy-efficient communication is computer\_networks. \\[2pt]16140 & \textbf{Autonomous Scientific Discovery}\newline {\footnotesize \texttt{autonomous\_\allowbreak scientific\_\allowbreak discovery}\newline area \texttt{computational\_\allowbreak science}} & AI and automation systems that perform or substantially drive the scientific process: AI scientist and research-agent systems, automated hypothesis generation and literature synthesis, self-driving laboratories and closed-loop experimentation, AI-driven discovery of materials, molecules, equations, algorithms or theorems, autonomous experimental design, benchmarks for research agents, and the epistemology and evaluation of machine-generated science. Using ML inside a scientific pipeline without autonomy is scientific\_computing or computational\_biology; LLM agents in general are ai\_agents. \\[2pt]162\end{longtable}163\end{small}164\end{singlespace}165166\subsection{Signal types and the assignment rule}167\label{app:taxonomy-rule}168169Every leaf lists weighted signals of five types, and the five types are the only evidence the classifier ever170sees. (i)~\emph{OpenAlex topics}: the up-to-three topic identifiers that OpenAlex attaches to a work, drawn171from any of the 26 OpenAlex fields and not only from field~17; a topic may appear under several leaves with172different weights. (ii)~\emph{OpenAlex subfields}: a coarse fallback used by 21 leaf--subfield entries over173eleven subfields (the ten field-17 subfields and subfield~1312, Molecular Biology, for computational biology),174always with a small weight. (iii)~\emph{arXiv categories}: the primary category and cross-lists of the linked175arXiv record, 162 entries over 49 distinct categories, namely176\texttt{cs.AI}, \texttt{cs.AR}, \texttt{cs.CC}, \texttt{cs.CE}, \texttt{cs.CG}, \texttt{cs.CL}, \texttt{cs.CR}, \texttt{cs.CV}, \texttt{cs.CY}, \texttt{cs.DB}, \texttt{cs.DC}, \texttt{cs.DL}, \texttt{cs.DM}, \texttt{cs.DS}, \texttt{cs.ET}, \texttt{cs.FL}, \texttt{cs.GR}, \texttt{cs.GT}, \texttt{cs.HC}, \texttt{cs.IR}, \texttt{cs.IT}, \texttt{cs.LG}, \texttt{cs.LO}, \texttt{cs.MA}, \texttt{cs.MM}, \texttt{cs.MS}, \texttt{cs.NA}, \texttt{cs.NE}, \texttt{cs.NI}, \texttt{cs.OS}, \texttt{cs.PF}, \texttt{cs.PL}, \texttt{cs.RO}, \texttt{cs.SD}, \texttt{cs.SE}, \texttt{cs.SI}, \texttt{cs.SY}, \texttt{eess.AS}, \texttt{eess.IV}, \texttt{eess.SP}, \texttt{eess.SY}, \texttt{math.IT}, \texttt{math.OC}, \texttt{math.ST}, \texttt{physics.comp-ph}, \texttt{q-bio.GN}, \texttt{q-bio.QM}, \texttt{quant-ph}, \texttt{stat.ML}. (iv)~\emph{DBLP streams}: the venue stream (journal or conference series,177the prefix of the DBLP key) of the linked DBLP record, 1{,}320 entries over 771 distinct streams.178(v)~\emph{Keyword patterns}: 273 regular expressions matched, in the present version of the ontology without179exception, on the concatenation of title and abstract (the YAML schema allows title-only and abstract-only180patterns, but none is used); 24 exclusion patterns veto a leaf when they match the same text.181182The rule itself is the one written out in equations~\eqref{eq:score} and~\eqref{eq:assign}: within each183signal type the weights of the matched signals are summed and capped at~1.0; the capped values of the five184types are summed; the leaf is assigned when the total reaches the threshold $\theta = 1.0$ and no exclusion185pattern matches; the primary leaf is the argmax, ties going to the child before the parent and then to the186lower category number. The weights are therefore to be read on a scale where 1.0 means ``this signal alone187suffices''. Table~\ref{tab:appA-signals} reports, for each leaf, how many signals of each type it lists and188how many of them carry weight~1.0; Table~\ref{tab:appA-area-signals} aggregates the same counts by area.189Three regularities are visible. First, every one of the forty leaves has at least one keyword pattern of190weight 1.0 (106 of the 273 patterns are at 1.0, the rest spread between 0.2 and 0.9), so that a title or191abstract naming the object of the category assigns it without metadata support; the build script refuses to192serialise an ontology in which this is false for the thirteen categories that the design singles out (the193six AI categories that OpenAlex topics do not separate and seven small categories without a topic or venue of194their own), and in version~1.0 the property happens to hold for all forty. Second, the number of topic195signals varies by an order of magnitude across leaves, from 4 for \texttt{compilers},196\texttt{reinforcement\_\allowbreak learning} and \texttt{federated\_\allowbreak learning} to 45 for \texttt{computer\_\allowbreak networks} and 44 for \texttt{computer\_\allowbreak vision}; this197mirrors the granularity of the OpenAlex topic model, which has many clusters for networking and vision and198none for federated learning, and not the importance of the categories. Third, venue signals are dense (291 of199the 1{,}320 stream entries are at weight 1.0, almost all for single-purpose venues) but concentrated on the200systems, networking and theory leaves, where the DBLP venue structure is informative, and sparse for the201post-2022 AI leaves, where the same few venues host everything.202203\begin{singlespace}204\begin{footnotesize}205\begin{longtable}{@{}r p{4.3cm} r r r r r r r r r r@{}}206\caption{Signals per category in \texttt{ontology.yaml}: number of weighted OpenAlex topics (T), of which with weight 1.0 (T$_1$); OpenAlex subfields (F); arXiv categories (X); DBLP streams (D), of which with weight 1.0 (D$_1$); keyword patterns (K), of which with weight 1.0 (K$_1$); exclusion patterns (Z); related edges (R). Computed from the YAML file.}207\label{tab:appA-signals}\\208\toprule209No. & Category & T & T$_1$ & F & X & D & D$_1$ & K & K$_1$ & Z & R \\210\midrule211\endfirsthead212\caption[]{Signals per category (continued).}\\213\toprule214No. & Category & T & T$_1$ & F & X & D & D$_1$ & K & K$_1$ & Z & R \\215\midrule216\endhead217\midrule218\multicolumn{12}{r}{continued on next page}\\219\endfoot220\bottomrule221\endlastfoot2221 & \texttt{llm} & 6 & 1 & 1 & 3 & 12 & 0 & 12 & 3 & 0 & 5 \\2232 & \texttt{foundation\_\allowbreak models} & 5 & 0 & 1 & 4 & 8 & 0 & 10 & 2 & 0 & 5 \\2243 & \texttt{multimodal\_\allowbreak ai} & 5 & 1 & 1 & 4 & 15 & 0 & 9 & 4 & 1 & 5 \\2254 & \texttt{generative\_\allowbreak ai} & 5 & 1 & 0 & 6 & 9 & 0 & 11 & 4 & 2 & 6 \\2265 & \texttt{ai\_\allowbreak agents} & 5 & 1 & 1 & 2 & 12 & 3 & 9 & 5 & 1 & 5 \\2276 & \texttt{reinforcement\_\allowbreak learning} & 4 & 1 & 0 & 5 & 8 & 0 & 9 & 3 & 1 & 5 \\2287 & \texttt{ml\_\allowbreak theory} & 20 & 1 & 1 & 4 & 51 & 2 & 11 & 1 & 0 & 6 \\2298 & \texttt{deep\_\allowbreak learning\_\allowbreak architectures} & 11 & 2 & 1 & 4 & 21 & 0 & 12 & 1 & 1 & 6 \\2309 & \texttt{computer\_\allowbreak vision} & 44 & 9 & 1 & 2 & 58 & 13 & 10 & 3 & 0 & 6 \\23110 & \texttt{nlp} & 22 & 4 & 1 & 2 & 29 & 13 & 8 & 2 & 0 & 5 \\23211 & \texttt{speech\_\allowbreak audio} & 10 & 3 & 1 & 4 & 16 & 5 & 5 & 3 & 0 & 4 \\23312 & \texttt{robotics} & 23 & 6 & 0 & 3 & 41 & 27 & 4 & 1 & 2 & 5 \\23413 & \texttt{hci} & 24 & 4 & 1 & 3 & 44 & 22 & 10 & 1 & 0 & 6 \\23514 & \texttt{software\_\allowbreak engineering} & 20 & 7 & 1 & 1 & 39 & 18 & 5 & 3 & 0 & 6 \\23615 & \texttt{programming\_\allowbreak languages} & 6 & 1 & 0 & 3 & 22 & 6 & 6 & 3 & 0 & 4 \\23716 & \texttt{compilers} & 4 & 0 & 0 & 5 & 25 & 2 & 5 & 3 & 0 & 5 \\23817 & \texttt{formal\_\allowbreak verification} & 10 & 1 & 0 & 4 & 43 & 10 & 7 & 5 & 0 & 6 \\23918 & \texttt{algorithms} & 35 & 2 & 1 & 7 & 68 & 9 & 10 & 2 & 0 & 6 \\24019 & \texttt{computational\_\allowbreak complexity} & 9 & 0 & 1 & 7 & 28 & 2 & 7 & 5 & 1 & 5 \\24120 & \texttt{database\_\allowbreak systems} & 21 & 1 & 1 & 1 & 36 & 9 & 5 & 2 & 1 & 5 \\24221 & \texttt{information\_\allowbreak retrieval} & 20 & 2 & 1 & 3 & 29 & 5 & 6 & 3 & 0 & 5 \\24322 & \texttt{distributed\_\allowbreak computing} & 16 & 2 & 1 & 4 & 51 & 15 & 7 & 3 & 1 & 8 \\24423 & \texttt{cloud\_\allowbreak computing} & 11 & 2 & 0 & 4 & 29 & 5 & 5 & 2 & 1 & 6 \\24524 & \texttt{edge\_\allowbreak computing} & 13 & 1 & 0 & 4 & 38 & 1 & 5 & 2 & 1 & 7 \\24625 & \texttt{computer\_\allowbreak networks} & 45 & 9 & 1 & 4 & 96 & 42 & 5 & 1 & 0 & 5 \\24726 & \texttt{cybersecurity} & 34 & 7 & 0 & 1 & 29 & 16 & 6 & 2 & 1 & 8 \\24827 & \texttt{applied\_\allowbreak cryptography} & 14 & 3 & 0 & 4 & 29 & 9 & 6 & 3 & 0 & 6 \\24928 & \texttt{blockchain} & 6 & 1 & 0 & 3 & 19 & 3 & 4 & 2 & 1 & 5 \\25029 & \texttt{computer\_\allowbreak architecture} & 21 & 4 & 1 & 5 & 59 & 6 & 4 & 2 & 1 & 7 \\25130 & \texttt{ai\_\allowbreak accelerators} & 11 & 1 & 0 & 5 & 44 & 1 & 5 & 3 & 1 & 7 \\25231 & \texttt{operating\_\allowbreak systems} & 12 & 0 & 0 & 5 & 39 & 6 & 4 & 2 & 1 & 6 \\25332 & \texttt{quantum\_\allowbreak computing} & 16 & 2 & 0 & 6 & 27 & 4 & 3 & 2 & 1 & 5 \\25433 & \texttt{scientific\_\allowbreak computing} & 41 & 4 & 1 & 6 & 57 & 10 & 4 & 2 & 0 & 8 \\25534 & \texttt{computational\_\allowbreak biology} & 34 & 3 & 1 & 3 & 60 & 12 & 3 & 2 & 0 & 7 \\25635 & \texttt{computer\_\allowbreak graphics} & 15 & 2 & 1 & 4 & 21 & 8 & 9 & 2 & 0 & 5 \\25736 & \texttt{privacy\_\allowbreak preserving\_\allowbreak computation} & 13 & 1 & 0 & 4 & 26 & 1 & 5 & 3 & 1 & 7 \\25837 & \texttt{federated\_\allowbreak learning} & 4 & 0 & 0 & 4 & 15 & 0 & 5 & 2 & 1 & 6 \\25938 & \texttt{ai\_\allowbreak safety} & 8 & 1 & 0 & 5 & 13 & 4 & 14 & 7 & 1 & 7 \\26039 & \texttt{sustainable\_\allowbreak computing} & 11 & 2 & 0 & 6 & 28 & 2 & 4 & 2 & 1 & 7 \\26140 & \texttt{autonomous\_\allowbreak scientific\_\allowbreak discovery} & 12 & 0 & 0 & 8 & 26 & 0 & 4 & 3 & 1 & 8 \\262\midrule263& Total (entries) & 646 & 93 & 21 & 162 & 1320 & 291 & 273 & 106 & 24 & 236 \\264\end{longtable}265\end{footnotesize}266\end{singlespace}267268\begin{table}[htbp]269\begin{singlespace}270\centering271\caption{Signal entries aggregated by area (sums of the rows of Table~\ref{tab:appA-signals}; a topic or venue mapped to several leaves of the same area is counted once per leaf).}272\label{tab:appA-area-signals}273\small274\begin{tabular}{l r r r r r}275\toprule276Area & Leaves & Topic entries & arXiv entries & DBLP entries & Keyword patterns \\277\midrule278\texttt{ai\_\allowbreak ml} & 9 & 69 & 37 & 149 & 97 \\279\texttt{perception\_\allowbreak interaction} & 6 & 138 & 18 & 209 & 46 \\280\texttt{software\_\allowbreak pl} & 4 & 40 & 13 & 129 & 23 \\281\texttt{theory} & 2 & 44 & 14 & 96 & 17 \\282\texttt{data\_\allowbreak distributed} & 6 & 126 & 20 & 279 & 33 \\283\texttt{security\_\allowbreak privacy} & 5 & 71 & 16 & 118 & 26 \\284\texttt{hardware\_\allowbreak emerging} & 5 & 71 & 27 & 197 & 20 \\285\texttt{computational\_\allowbreak science} & 3 & 87 & 17 & 143 & 11 \\286\bottomrule287\end{tabular}288\end{singlespace}289\end{table}290291The weight scale has a second consequence that the reader of Part~IX must keep in mind. A signal with weight292below 1.0 can only contribute through corroboration by a signal of \emph{another} type, because repetitions293within a type are capped. The design relies on this in both directions: a general machine-learning venue at2940.3 and the \texttt{cs.LG} category at 0.2 never assign \texttt{ml\_\allowbreak theory} by themselves, whereas a 0.6 topic295and a 0.4 arXiv category do reach the threshold. One discrepancy between the documentation and the296implementation of the within-type aggregation (maximum versus sum over matched topics of the same leaf) is297recorded in Section~\ref{sec:m-score}; the implemented rule, the sum, is the one described here and the one298fixed by the unit tests.299300\subsection{Coverage of the OpenAlex topic vocabulary}301\label{app:taxonomy-openalex}302303The OpenAlex export of 2026-10-14 contains 4{,}516 topics, 302 of them in field~17 (Computer Science). The304ontology maps 240 of the 302 (79.5\,\%) to at least one leaf and leaves 62 unmapped with an explicit reason305each (Section~\ref{app:taxonomy-unmapped}). Table~\ref{tab:appA-subfields} breaks the field-17 coverage down306by subfield. Coverage is complete for Computer Vision and Pattern Recognition, Hardware and Architecture,307Human--Computer Interaction, Signal Processing and Software, and nearly complete for Computational Theory and308Mathematics; it is lowest for Information Systems (45 of 78), the subfield into which OpenAlex places most of309its educational-technology and media clusters, and for Artificial Intelligence (61 of 77), where the unmapped310topics are physical-science, generic residual or empty clusters rather than AI research.311312\begin{table}[htbp]313\begin{singlespace}314\centering315\caption{Coverage of the OpenAlex field~17 (Computer Science) topics by subfield: number of topics in the 2026-10-14 export, mapped to at least one category, and left unmapped with a stated reason (Table~\ref{tab:appA-unmapped}).}316\label{tab:appA-subfields}317\small318\begin{tabular}{l l r r r}319\toprule320Subfield & Name & Topics & Mapped & Unmapped \\321\midrule3221702 & Artificial Intelligence & 77 & 61 & 16 \\3231703 & Computational Theory and Mathematics & 24 & 23 & 1 \\3241704 & Computer Graphics and Computer-Aided Design & 4 & 3 & 1 \\3251705 & Computer Networks and Communications & 43 & 35 & 8 \\3261706 & Computer Science Applications & 10 & 7 & 3 \\3271707 & Computer Vision and Pattern Recognition & 35 & 35 & 0 \\3281708 & Hardware and Architecture & 8 & 8 & 0 \\3291709 & Human-Computer Interaction & 8 & 8 & 0 \\3301710 & Information Systems & 78 & 45 & 33 \\3311711 & Signal Processing & 10 & 10 & 0 \\3321712 & Software & 5 & 5 & 0 \\333\midrule334& Field 17 total & 302 & 240 & 62 \\335\bottomrule336\end{tabular}337\end{singlespace}338\end{table}339340Topics outside field~17 matter because computer-science work is frequently assigned a primary topic from341Engineering, Mathematics, Physics or the life sciences, and because several leaves (computational biology,342scientific computing, quantum computing, speech and audio, robotics) have their natural OpenAlex home outside343field~17. The ontology therefore maps 170 further topics from eighteen other fields344(Table~\ref{tab:appA-fields}), Engineering (71) and Mathematics (20) being the largest contributors, for a345total of 410 distinct mapped topics and 646 topic--leaf weights. These adjacent topics were selected by346reading the OpenAlex keyword lists of about 120 candidate topics named in the area modules; the remaining347roughly 4{,}000 topics of the export were not inspected, which is the main known gap of the mapping348(Section~\ref{app:taxonomy-limits}). Of the 410 mapped topics, 146 point to more than one leaf, up to six for349the broadest clusters (for instance T10054, \emph{Parallel Computing and Optimization Techniques}, scores in350\texttt{computer\_\allowbreak architecture} at 0.8, \texttt{compilers} and \texttt{distributed\_\allowbreak computing} at 0.6,351\texttt{scientific\_\allowbreak computing} at 0.4, \texttt{ai\_\allowbreak accelerators} at 0.3 and \texttt{operating\_\allowbreak systems} at3520.2). The full audit list of multiply mapped topics is the last generated section of \texttt{ONTOLOGY.md};353it is the first place to look when a co-classification pattern in Part~IX seems suspicious, because a topic354mapped to two leaves at weight 1.0 produces that pair mechanically.355356\begin{table}[htbp]357\begin{singlespace}358\centering359\caption{Distinct OpenAlex topics mapped to at least one category, by OpenAlex field of the topic (\texttt{topic\_map.csv}).}360\label{tab:appA-fields}361\small362\begin{tabular}{r l r}363\toprule364Field & Name & Mapped topics \\365\midrule36617 & Computer Science & 240 \\36722 & Engineering & 71 \\36826 & Mathematics & 20 \\36913 & Biochemistry, Genetics and Molecular Biology & 18 \\37031 & Physics and Astronomy & 15 \\37133 & Social Sciences & 10 \\37218 & Decision Sciences & 8 \\37327 & Medicine & 6 \\37412 & Arts and Humanities & 5 \\37528 & Neuroscience & 4 \\37623 & Environmental Science & 3 \\37719 & Earth and Planetary Sciences & 2 \\37832 & Psychology & 2 \\37911 & Agricultural and Biological Sciences & 1 \\38014 & Business, Management and Accounting & 1 \\38120 & Economics, Econometrics and Finance & 1 \\38221 & Energy & 1 \\38325 & Materials Science & 1 \\38436 & Health Professions & 1 \\385\midrule386& All fields & 410 \\387\bottomrule388\end{tabular}389\end{singlespace}390\end{table}391392\subsection{Coverage of the DBLP venue vocabulary}393\label{app:taxonomy-dblp}394395DBLP streams are the venue signal. The counts quoted in this subsection and in Table~\ref{tab:appA-dblp-unmapped} are ontology-build statistics copied from the generated ``DBLP coverage'' section of \texttt{taxonomy/ONTOLOGY.md} (computed at build time from \texttt{dblp\_streams\_2018\_2026.csv}); they describe the mapping, are not corpus quantities, and are not regenerated by \texttt{tables/make\_numbers.py}. The stream table from which the mapping was built lists every DBLP396journal or conference series with at least one hundred records in 2018--2026 (3{,}569 streams); the ontology397maps 771 of them. Measured on the DBLP records of 2018--2026 and excluding CoRR (the arXiv mirror stream,398which carries no venue information), the mapped streams cover 2{,}017{,}910 of 3{,}319{,}884 records399(60.8\,\%). Among the 599 largest streams, 496 are mapped (82.8\,\%); the 103 unmapped large streams400(Table~\ref{tab:appA-dblp-unmapped} lists the 25 largest) are of two kinds. Some are out of scope for the401forty categories although DBLP indexes them: control theory (\emph{IEEE Transactions on Automatic Control},402\emph{Automatica}, \emph{IEEE Control Systems Letters}), instrumentation and industrial electronics,403geo-information, agriculture, production research, information-systems and engineering-education404conferences (HICSS, AMCIS, ICIS, FIE). Others are too generic to be a category signal at any weight, the405largest being \emph{IEEE Access} with 108{,}838 records, followed by \emph{Information}, \emph{Complexity},406\emph{Systems} and \emph{PeerJ Computer Science}; works published there are classified from their topics,407arXiv categories and text alone. A record in an unmapped stream is therefore not excluded from the corpus408(scope is defined in Part~III, not here); it simply receives no venue evidence. Because weights are assigned409per stream, workshop volumes that share a stream key with a flagship conference inherit the flagship's410weight, a known coarseness of the venue signal.411412\begin{table}[htbp]413\begin{singlespace}414\centering415\caption{The 25 largest DBLP streams (2018--2026 records, CoRR excluded) deliberately left unmapped, out of 103 unmapped streams among the 599 largest (\texttt{ONTOLOGY.md}, generated section ``DBLP coverage'').}416\label{tab:appA-dblp-unmapped}417\small418\begin{tabular}{l r l}419\toprule420Stream & Records & Venue \\421\midrule422\texttt{journals/access} & 108,838 & IEEE Access \\423\texttt{journals/tim} & 13,470 & IEEE Trans. Instrum. Meas. \\424\texttt{journals/tie} & 10,844 & IEEE Trans. Ind. Electron. \\425\texttt{conf/hicss} & 7,816 & HICSS \\426\texttt{conf/iecon} & 7,727 & IECON \\427\texttt{journals/cea} & 7,291 & Comput. Electron. Agric. \\428\texttt{conf/smc} & 6,048 & SMC \\429\texttt{journals/complexity} & 5,786 & Complex. \\430\texttt{journals/ress} & 5,732 & Reliab. Eng. Syst. Saf. \\431\texttt{journals/tac} & 5,687 & IEEE Trans. Autom. Control. \\432\texttt{journals/information} & 5,621 & Inf. \\433\texttt{journals/taes} & 5,543 & IEEE Trans. Aerosp. Electron. Syst. \\434\texttt{journals/jfi} & 5,432 & J. Frankl. Inst. \\435\texttt{journals/ijgi} & 5,118 & ISPRS Int. J. Geo Inf. \\436\texttt{journals/automatica} & 4,727 & Autom. \\437\texttt{conf/eucc} & 4,160 & ECC \\438\texttt{conf/iccsa} & 3,817 & ICCSA (4) \\439\texttt{conf/fie} & 3,807 & FIE \\440\texttt{conf/amcis} & 3,798 & AMCIS \\441\texttt{journals/peerj-cs} & 3,768 & PeerJ Comput. Sci. \\442\texttt{journals/systems} & 3,667 & Syst. \\443\texttt{journals/csysl} & 3,647 & IEEE Control. Syst. Lett. \\444\texttt{journals/aei} & 3,628 & Adv. Eng. Informatics \\445\texttt{journals/ijpr} & 3,591 & Int. J. Prod. Res. \\446\texttt{journals/sj} & 3,558 & IEEE Syst. J. \\447\bottomrule448\end{tabular}449\end{singlespace}450\end{table}451452\subsection{Unmapped field-17 topics and the reasons}453\label{app:taxonomy-unmapped}454455OpenAlex places in field~17 a number of clusters that are not computer-science research. The ontology leaves45662 field-17 topics unmapped and records a reason for each; Table~\ref{tab:appA-reasons} groups the reasons457into eight codes and Table~\ref{tab:appA-unmapped} lists the topics. The dominant case, 29 topics, is458education and e-learning research (pedagogy, learning outcomes, teacher development, educational459technology studied as an intervention rather than as software); such work is not one of the forty categories,460and educational technology \emph{as software} is still captured through software-engineering and HCI venues461and keywords when the contribution is technical. Nine topics are physical-science or engineering clusters462without computing content (sensor instrumentation, photovoltaics, geochemistry, seismology, nonlinear463dynamics, control theory, transportation safety, chemical and environmental engineering). Five are generic464residual labels (``Computer Science and Engineering'', ``Diverse Interdisciplinary Research Studies'',465``Mathematics, Computing, and Information Processing'', ``Statistical and Computational Modeling'',466``Advanced Technologies and Applied Computing'') whose keyword lists carry no specific content; works bearing467them reach categories through their other topics, venue, arXiv category and text. Four are media,468communication, literary or philosophical clusters, four are out-of-scope disciplines (psychiatry, economic469growth, cognitive science, pandemic health care), one is the history of computing (kept out of the470research-category map but relevant to Part~II), one is the small cognitive-informatics cluster that has no471counterpart among the forty leaves and was not forced into \texttt{ai\_\allowbreak agents}, and nine are residual topics472with zero works in the export. The works column of Table~\ref{tab:appA-unmapped} is the OpenAlex473\texttt{works\_\allowbreak count} of the topic over all years and fields; it is reported to show the scale of what is set474aside (the education cluster alone exceeds one million works, the media cluster T13745 alone 204{,}789) and is475not a count of corpus records. The hand-written design note of \texttt{ONTOLOGY.md} speaks of 63 unmapped476clusters; the generated tables, which are authoritative, list 62.477478\begin{table}[htbp]479\begin{singlespace}480\centering481\caption{Reason codes for the unmapped field~17 topics (Table~\ref{tab:appA-unmapped}), with the number of topics and the sum of their OpenAlex \texttt{works\_\allowbreak count}.}482\label{tab:appA-reasons}483\small484\begin{tabularx}{\textwidth}{c X r r}485\toprule486Code & Reason recorded in the ontology & Topics & Works (sum) \\487\midrule488E & Education / e-learning research; not a CS research category; technical ed-tech captured elsewhere & 29 & 1{,}027{,}591 \\489P & Physical-science or engineering cluster without computing contribution, placed in field~17 by OpenAlex & 9 & 352{,}761 \\490Z & Residual OpenAlex topic with zero works in the export; nothing to map & 9 & 0 \\491G & Generic, uninformative residual label; works reach categories through other signals & 5 & 30{,}206 \\492M & Media, communication, cultural, literary or philosophy-of-science cluster; not CS research & 4 & 275{,}159 \\493O & Out-of-scope discipline (psychiatry, economics, cognitive science, pandemic health care) & 4 & 33{,}228 \\494H & History of computing technologies (history of science); background for Part~II only & 1 & 72{,}582 \\495C & Cognitive informatics / brain informatics; idiosyncratic cluster with no counterpart among the leaves & 1 & 13{,}053 \\496\midrule497& Total & 62 & 1{,}804{,}580 \\498\bottomrule499\end{tabularx}500\end{singlespace}501\end{table}502503\begin{singlespace}504\begin{footnotesize}505\begin{longtable}{@{}l p{7.4cm} l r l@{}}506\caption{The 62 OpenAlex field~17 topics left unmapped, with the subfield, the number of works of the topic in the 2026-10-14 topic export (all years, all fields) and the reason code of Table~\ref{tab:appA-reasons}.}507\label{tab:appA-unmapped}\\508\toprule509Topic & Name & Subfield & Works & Reason \\510\midrule511\endfirsthead512\caption[]{Unmapped field~17 topics (continued).}\\513\toprule514Topic & Name & Subfield & Works & Reason \\515\midrule516\endhead517\midrule518\multicolumn{5}{r}{continued on next page}\\519\endfoot520\bottomrule521\endlastfoot522T13745 & Media and Digital Communication & 1705 & 204,789 & M \\523T14496 & Educational Technology and E-Learning & 1710 & 163,032 & E \\524T14516 & English Language Learning and Teaching & 1710 & 107,202 & E \\525T14430 & Higher Education and Teaching Methods & 1710 & 103,711 & E \\526T12797 & History of Computing Technologies & 1706 & 72,582 & H \\527T12564 & Sensor Technology and Measurement Systems & 1705 & 71,005 & P \\528T11276 & Solar Radiation and Photovoltaics & 1702 & 70,601 & P \\529T14455 & Technology and Education Systems & 1705 & 70,588 & E \\530T12157 & Geochemistry and Geologic Mapping & 1702 & 69,914 & P \\531T12171 & Open Education and E-Learning & 1706 & 66,332 & E \\532T14498 & Educational Technology in Learning & 1710 & 63,096 & E \\533T14216 & Multimedia Learning Systems & 1710 & 62,005 & E \\534T11187 & Nonlinear Dynamics and Pattern Formation & 1705 & 61,626 & P \\535T14103 & Educational Management and Quality & 1710 & 57,729 & E \\536T11370 & Mobile Learning in Education & 1710 & 55,961 & E \\537T12408 & Educational Innovations and Challenges & 1710 & 53,673 & E \\538T12395 & Educational Innovations and Technology & 1710 & 45,138 & E \\539T14025 & Educational Technology and Assessment & 1710 & 37,519 & E \\540T13602 & Educational Methods and Media Use & 1710 & 35,313 & E \\541T13194 & ICT in Developing Communities & 1710 & 32,298 & M \\542T13615 & Transportation Systems and Safety & 1704 & 26,785 & P \\543T14215 & Chemical and Environmental Engineering Research & 1710 & 25,315 & P \\544T13018 & Seismology and Earthquake Studies & 1702 & 23,104 & P \\545T13081 & Digital Education and Society & 1710 & 22,995 & E \\546T14029 & Thoreau and American Literature & 1710 & 19,567 & M \\547T13386 & Educational Methods and Teacher Development & 1710 & 19,565 & E \\548T14345 & Scientific Research and Philosophical Inquiry & 1710 & 18,505 & M \\549T13016 & Innovative Educational Technologies & 1710 & 16,953 & E \\550T13674 & Computer Science and Engineering & 1702 & 16,620 & G \\551T13559 & Edcuational Technology Systems & 1702 & 16,507 & E \\552T14381 & Psychiatry, Mental Health, Neuroscience & 1702 & 15,800 & O \\553T14519 & Innovations in Education and Learning Technologies & 1710 & 14,509 & E \\554T13062 & Cognitive Computing and Networks & 1702 & 13,053 & C \\555T13867 & Economic Growth and Development & 1710 & 9,780 & O \\556T13898 & Diverse Interdisciplinary Research Studies & 1702 & 6,495 & G \\557T14246 & E-Learning and Knowledge Management & 1706 & 6,373 & E \\558T13523 & Mathematics, Computing, and Information Processing & 1703 & 6,343 & G \\559T12805 & Cognitive Science and Mapping & 1702 & 5,430 & O \\560T13935 & Mathematical Control Systems and Analysis & 1702 & 4,409 & P \\561T14409 & Educational Systems and Policies & 1710 & 3,650 & E \\562T13565 & Education and Learning Interventions & 1710 & 2,867 & E \\563T14071 & Healthcare during COVID-19 Pandemic & 1710 & 2,218 & O \\564T14403 & Educational Challenges and Innovations & 1710 & 1,150 & E \\565T13482 & Engineering Education and Technology & 1710 & 765 & E \\566T14351 & Statistical and Computational Modeling & 1702 & 731 & G \\567T13932 & Education, Technology, and Economics & 1710 & 577 & E \\568T13873 & Educational Methods and Technology & 1710 & 329 & E \\569T13676 & Educational and Technological Research & 1710 & 52 & E \\570T13345 & Advanced Technologies and Applied Computing & 1705 & 17 & G \\571T14387 & Environmental Engineering and Cultural Studies & 1710 & 2 & P \\572T13514 & Experience-Based Knowledge Management & 1702 & 0 & Z \\573T13567 & AI and Multimedia in Education & 1702 & 0 & E \\574T13693 & Smart Systems and Machine Learning & 1705 & 0 & Z \\575T13748 & Advanced Statistical Modeling Techniques & 1705 & 0 & Z \\576T13836 & Educational Research and Pedagogy & 1705 & 0 & E \\577T13964 & Educational Technology and Optimization & 1710 & 0 & E \\578T14064 & Organizational and Employee Performance & 1702 & 0 & Z \\579T14155 & Advanced Technology in Applications & 1710 & 0 & Z \\580T14174 & Artificial Intelligence and Decision Support Systems & 1710 & 0 & Z \\581T14175 & Intuitionistic Fuzzy Systems Applications & 1702 & 0 & Z \\582T14413 & Advanced Technologies in Various Fields & 1702 & 0 & Z \\583T14431 & Diverse Research and Applications & 1710 & 0 & Z \\584\end{longtable}585\end{footnotesize}586\end{singlespace}587588\subsection{Known ambiguities and the decisions taken}589\label{app:taxonomy-ambiguities}590591Forty categories drawn up for a mandate are not a partition of the literature, and several pairs or triples592overlap by construction. The ontology's author resolved each overlap by a stated rule rather than by leaving593it to the weights; the rules are recorded here verbatim in substance, because they determine how the594multi-label statistics of Part~IX must be read.595596\paragraph{Large language models, foundation models, generative AI and NLP.}597\texttt{foundation\_\allowbreak models} is the parent concept (any modality, adaptation-oriented); \texttt{llm} is its598language child and the home of the ChatGPT/GPT/LLaMA-era literature; \texttt{generative\_\allowbreak ai} concerns the599generative model and its outputs (diffusion, GANs, text-to-X synthesis, deepfakes and detection), not text600generation by LLMs as such; \texttt{nlp} keeps task-framed language work. Multi-label assignment is expected601and intended: an instruction-tuning paper scores in \texttt{llm} and \texttt{nlp}, a diffusion language model602in \texttt{llm} and \texttt{generative\_\allowbreak ai}. The OpenAlex topic T10028, labelled ``Topic Modeling'', is in603fact the cluster hosting BERT, pre-trained language models and retrieval-augmented generation and is weighted6041.0 for \texttt{llm}; a reader who consults the OpenAlex label alone would misjudge this choice.605606\paragraph{Agents, reinforcement learning and robotics.}607Software autonomy (LLM agents, multi-agent systems, planning) is \texttt{ai\_\allowbreak agents}; learning from reward608is \texttt{reinforcement\_\allowbreak learning}; physical embodiment is \texttt{robotics}. The word ``agent'' never609counts on its own: the patterns require a computing qualifier (``LLM agent'', ``autonomous agent'',610``multi-agent system'') and an exclusion vetoes chemical, medical, commercial and other non-computing611senses (reagent, contrast agent, real-estate agent).612613\paragraph{AI safety, cybersecurity and the statistical side of interpretability.}614Adversarial examples, backdoors and prompt injection score in both \texttt{ai\_\allowbreak safety} and615\texttt{cybersecurity} at 0.8, so that either a second signal or the text decides; fairness and governance616belong to \texttt{ai\_\allowbreak safety}; explainability as a statistical method is \texttt{ml\_\allowbreak theory} (0.5), whereas617interpretability of large models as a safety practice is \texttt{ai\_\allowbreak safety} (1.0).618619\paragraph{Cloud, distributed and edge computing.}620\texttt{distributed\_\allowbreak computing} is the parent (algorithms, HPC systems software, consensus);621\texttt{cloud\_\allowbreak computing} is the service model (virtualisation, serverless, data-centre resource management);622\texttt{edge\_\allowbreak computing} is placement near users and devices (offloading, multi-access edge computing,623on-device inference). Internet-of-things topics are shared between \texttt{edge\_\allowbreak computing} and624\texttt{computer\_\allowbreak networks}.625626\paragraph{Cryptography, security and privacy.}627Primitives and protocols are \texttt{applied\_\allowbreak cryptography}; attacks, defences and systems hardening are628\texttt{cybersecurity}; using cryptography or statistics to protect data is629\texttt{privacy\_\allowbreak preserving\_\allowbreak computation}, whose child \texttt{federated\_\allowbreak learning} has no OpenAlex topic630and no venue of its own and is carried by keywords. The topic T10764 (``Privacy-Preserving Technologies in631Data'') hosts both the parent (1.0) and the child (0.7).632633\paragraph{Architecture, accelerators and operating systems.}634Accelerators are a child of architecture and include efficient-inference \emph{systems} (serving engines,635attention kernels, quantisation for hardware); operating systems have no OpenAlex topic and are carried by636the OSDI, SOSP, EuroSys, USENIX ATC and FAST streams, by \texttt{cs.OS} and by kernel and file-system keywords.637638\paragraph{Quantum computing and quantum physics.}639Only computing and information-processing framings count. Physics topics on qubit platforms are weighted6400.2--0.3, and an anchored exclusion removes texts about quantum dots, quantum chemistry, quantum field theory641and similar physical senses whenever no computing term (qubit, quantum algorithm, error correction, QKD,642NISQ, \ldots) appears anywhere in the title or abstract.643644\paragraph{Scientific computing, computational biology and autonomous discovery.}645A numerical method is \texttt{scientific\_\allowbreak computing}; a biological object is \texttt{computational\_\allowbreak biology};646AI that drives the scientific process itself (AI-scientist systems, self-driving laboratories, automated647hypothesis generation, AI-discovered algorithms or theorems) is \texttt{autonomous\_\allowbreak scientific\_\allowbreak discovery},648which is keyword-driven and vetoed when no AI or automation term is present.649650\paragraph{Algorithms and complexity.}651STOC, FOCS and ICALP are shared; ECCC, \emph{ToCT} and \texttt{cs.CC} are complexity-only. Metaheuristics and652evolutionary computation are counted as \texttt{algorithms} at 0.5--0.6 because the corpus is expected to653contain many such works and no other leaf claims them.654655\paragraph{Education, media and other field-17 residues.}656As documented in Section~\ref{app:taxonomy-unmapped}, the educational, media, physical-science and generic657clusters that OpenAlex places in field~17 are left unmapped, with four exceptions mapped at low weight; this658is a deliberate narrowing of ``computer science'' relative to the OpenAlex field, and its effect on corpus659scope is examined in Part~III, where scope is defined by DBLP membership and OpenAlex primary field rather660than by the ontology.661662\subsection{Limitations of the mapping and the validation design}663\label{app:taxonomy-limits}664665The weights are expert priors set by reading keyword lists and venue scopes, not parameters fitted to666labelled data, and the ontology's own documentation says so. Three limitations follow and are repeated here667because they bound what the classification can show. Venue weights are per DBLP stream, so a workshop sharing668a stream key with its main conference inherits the main conference's weight. OpenAlex keyword lists were read669in full for the 302 field-17 topics and for about 120 CS-adjacent topics named in the area modules, but not for670the remaining roughly 4{,}000 topics; relevant clusters from Medicine or Materials Science may be missing and671are to be added when the corpus reveals them, through an amendment. And keyword patterns on abstracts fire on672mentions as well as on contributions, a failure mode discussed with the others in673Section~\ref{sec:m-classification}.674675The ontology is to be validated, after the classification run, on a stratified sample whose design is fixed in676\texttt{taxonomy/VALIDATION.md}: fifty eligible works with an abstract per rule-assigned primary category677(forty strata) plus two hundred works to which the rules assigned no category, drawn with seed~44; two678independent LLM raters that never see the rule-based assignment and receive only the definitions of679Table~\ref{tab:appA-leaves} with the title, abstract, venue and year of each work; the principal investigator680as third rater on a 200-work subsample (checkpoint H-2). The pre-specified decision rule is that a category681with consensus $F_1 < 0.60$ triggers a mapping revision, logged as a protocol amendment, and a re-run of the682classification. The agreement statistics, the per-category precision and recall, the list of categories that683met or failed the decision rule, and any amendment that resulted are reported in Part~IX; the validation had684not been run when this appendix was written, and nothing about its outcome is asserted here.685