{ "model": { "rank": 4655, "code": "thing-q197536", "model_id": "vr.tr.deep-learning", "name": "deep learning", "purpose": "Enable an AI agent to recognise deep learning approaches, assess the evidence supporting their capabilities, and determine appropriate training, adaptation, evaluation and deployment actions.", "family": "Thing Registry", "category": "Information and virtual systems", "status": "research-draft", "kind": "thing", "plane": "INF", "domain": "INF.MED", "industry": "", "version": "", "url": "/models/thing/q197536/", "tier": 2, "score": 73, "payload": { "layer": "wikidata", "aliases": [ "deep transfer learning", "deep reinforcement learning", "topological deep learning" ], "aliasCount": 3, "merged": 3, "knownIn": 73, "facets": null, "markers": [], "lexicalClass": "", "senseRank": null, "alsoRegisteredAs": null, "source": { "dataset": "wikidata", "item": "Q197536", "url": "https://www.wikidata.org/wiki/Q197536", "license": "CC0 1.0" } }, "research": { "vercy": "1.0-draft", "publication": { "status": "research-draft", "adjudicationStatus": "unreviewed", "publishableCanonical": false, "generatedAt": "2026-09-09T20:43:27Z", "providers": [ "Codex" ], "breadth": "recalled by Codex without web access - no source was read", "missingProviders": [], "pass": 2, "cost": { "grok": { "seconds": 20.9, "error": "Reading additional input from stdin...\nOpenAI Codex v0.153.4\n--------\nworkdir: R:\\02_PROJECTS\\02_Meta_Models_Platforms\\Ver.cy\\current\\thing-registry-backlog\nmodel: gpt-6-astra\nprovider: openai\napproval: never\nsandbox: read-only\nreasoning effort: none\nreasoning summaries: none\nsession id: 01a087e8-3e19-7883-976e-c9d00e210f96\n--------\nuser\nDescribe what is already known about one registered thing. Answer as JSON only, no prose around it.\n\nThing: deep learning\nSense to describe: (none recorded)\nDomain code: INF.MED\nAlso known as: (none)\n\n\nContext for this batch of 1001 things:\n# Batch 005: 1001 r", "usd": 0, "recall": true }, "codex": { "seconds": 68.5, "error": "Reading additional input from stdin...\nOpenAI Codex v0.153.4\n--------\nworkdir: R:\\02_PROJECTS\\02_Meta_Models_Platforms\\Ver.cy\\current\\thing-registry-backlog\nmodel: gpt-6-astra\nprovider: openai\napproval: never\nsandbox: read-only\nreasoning effort: none\nreasoning summaries: none\nsession id: 01a087e8-3e0c-7103-9595-3a90f6b05b51\n--------\nuser\nYou are drafting a Vercy meta-model for one registered thing. Answer as JSON only, no prose around it.\n\nThing: deep learning\nRegistry id: vr.tr.deep-learning\nPlane / domain: INF / INF.MED\nRegistry definition: (none recorded)\nNames folded into this entry: (none" } } }, "metaModel": { "id": "THING-Q197536", "registryId": "vr.tr.deep-learning", "name": "deep learning", "version": "0.1.0-research.1", "entryKind": "thing", "family": "Thing Registry", "domain": [ "INF.MED" ], "status": "research-draft" }, "canonicalUrl": "https://ver.cy/models/thing/q197536/", "model": { "registry_id": "vr.tr.deep-learning", "name": "deep learning", "purpose": "Enable an AI agent to recognise deep learning approaches, assess the evidence supporting their capabilities, and determine appropriate training, adaptation, evaluation and deployment actions.", "definition": "Deep learning is a family of machine-learning methods that use neural networks with multiple processing layers to learn representations of data for tasks such as prediction, classification, and generation.", "scope_statement": "This model owns deep learning as a family of machine learning methods that learn representations through multiple computational layers, including the relationships among architectures, training procedures, trained models and demonstrated behaviour; it does not treat deep learning as an individual creative work or software artifact.", "in_scope": [ "Recognition of deep learning through learned representations and layered computation", "Architecture choices and their relationship to data structure and tasks", "Training objectives, optimisation and adaptation procedures", "Data dependencies and their effects on learned behaviour", "Evaluation evidence, generalisation and failure conditions", "Inference requirements and conditions for operational use" ], "out_of_scope": [ "Machine learning methods in general, including methods without deep representation learning", "Dataset stewardship, licensing and personal-data governance as independently managed subjects", "Processor design, accelerator manufacture and computing infrastructure management", "Software package maintenance and distribution as software lifecycle subjects", "Application-domain decisions and accountability, such as clinical diagnosis or credit approval", "Authorship, editions and rights of papers, code releases or generated works as independently registered artifacts" ], "distinguishing_features": [ "Identify multiple computational layers involved in learning representations; a long sequence of manually specified processing steps alone does not establish deep learning.", "Establish which representations are learned from data or feedback rather than supplied entirely as fixed, engineered features.", "Distinguish the architecture and learning procedure from a trained checkpoint: the same architecture can yield different behaviour after different training.", "Check whether the approach is generative, predictive or otherwise task-directed; generation and language modelling are applications of deep learning rather than defining requirements.", "Separate neural depth from parameter count: a large number of parameters alone does not establish a deep architecture." ], "characteristics": [ { "name": "Representation-learning structure", "kind": "category", "unit_or_values": "Learned multilayer representations; mixed learned and fixed representations; unclear", "why_it_matters": "Supports recognition of the method without relying solely on its marketing label." }, { "name": "Computational depth", "kind": "measurement", "unit_or_values": "Number of layers or sequential transformations, with counting convention and treatment of recurrence stated", "why_it_matters": "Describes layered computation while making architecture-dependent counting explicit." }, { "name": "Architecture family", "kind": "category", "unit_or_values": "Convolutional; recurrent; attention-based; graph neural; multilayer perceptron; hybrid; other specified", "why_it_matters": "Connects structural assumptions to the data and tasks the approach can accommodate." }, { "name": "Learning signal", "kind": "category", "unit_or_values": "Supervised; self-supervised; unsupervised; reinforcement-based; mixed, recorded per training stage", "why_it_matters": "Explains where the training target or feedback originates." }, { "name": "Parameter scale", "kind": "measurement", "unit_or_values": "Total, trainable and active parameters per input, each counted separately", "why_it_matters": "Helps assess adaptation scope, storage and computational demands without equating size with capability." }, { "name": "Training lineage", "kind": "relation", "unit_or_values": "Links among training runs, source checkpoints, data versions and adaptation stages", "why_it_matters": "Makes inherited behaviour and the provenance of a particular model state traceable." }, { "name": "Evaluation standing", "kind": "state", "unit_or_values": "Unevaluated; evaluated for stated conditions; acceptance criteria met; acceptance criteria failed; evidence outdated", "why_it_matters": "Prevents performance demonstrated in one setting from being treated as unrestricted fitness." }, { "name": "Inference resource demand", "kind": "measurement", "unit_or_values": "Latency in milliseconds, throughput in specified inputs per second, peak memory in bytes and energy in joules per specified workload", "why_it_matters": "Determines whether a trained model can operate within the intended environment." } ], "affordances": [ "Classify a proposed approach using evidence about its architecture and learned representations.", "Compare candidate architectures against task structure, available data and resource constraints.", "Specify and trace training or adaptation runs to their data, objectives and source checkpoints.", "Design evaluations that test generalisation, leakage, robustness and intended-use requirements.", "Select inference or compression configurations subject to measured capability and resource limits.", "Gate, monitor or suspend a deployment when its evaluation conditions or operating assumptions cease to hold." ] }, "sources": [], "structure": { "bundles": [ { "id": "identity-and-boundaries", "name": "Identity and boundaries", "description": "Recognise the deep learning method and separate it from related methods and its concrete artifacts.", "rationale": "An agent must identify what makes an approach deep learning before transferring claims about its capabilities or use.", "layers": [ { "id": "learned-representation-test", "name": "Learned representation test", "description": "Examine layered computation and the origin of intermediate representations.", "findings": [ { "id": "evidence-of-deep-learning", "name": "Evidence of deep learning", "description": "Record which computational stages learn representations and the convention used to describe their depth.", "questions": [ { "text": "Which intermediate representations are learned, and through which sequence of computational layers?", "kind": "definition", "id": "evidence-of-deep-learning-q01" }, { "text": "What distinguishes this approach from shallow learning over engineered features or a fixed multistage pipeline?", "kind": "boundary", "id": "evidence-of-deep-learning-q02" } ] } ] }, { "id": "method-and-instance", "name": "Method and instance", "description": "Separate the field, architecture, training procedure and trained model state.", "findings": [ { "id": "claim-attachment", "name": "Claim attachment", "description": "Attach each capability claim to the particular method or model state for which evidence exists.", "questions": [ { "text": "Does this description concern an architecture family, a training recipe, a training run or a particular checkpoint?", "kind": "boundary", "id": "claim-attachment-q01" }, { "text": "Which identified checkpoint and inference configuration support each reported capability?", "kind": "provenance", "id": "claim-attachment-q02" } ] } ] } ] }, { "id": "architecture-and-representations", "name": "Architecture and representations", "description": "Describe how a neural architecture transforms inputs and what structural assumptions it introduces.", "rationale": "Architecture affects admissible inputs, information flow, scaling and the kinds of relationships a model can learn.", "layers": [ { "id": "computational-structure", "name": "Computational structure", "description": "Record computational blocks, connectivity and parameter sharing.", "findings": [ { "id": "architecture-assumptions", "name": "Architecture assumptions", "description": "Identify structural choices such as convolution, recurrence, attention, graph message passing and conditional computation.", "questions": [ { "text": "Which blocks, connections and parameter-sharing rules define the architecture?", "kind": "definition", "id": "architecture-assumptions-q01" }, { "text": "Which assumptions about locality, order, symmetry or relationships are imposed by those choices?", "kind": "boundary", "id": "architecture-assumptions-q02" } ] } ] }, { "id": "input-and-latent-structure", "name": "Input and latent structure", "description": "Describe input encoding, internal representations and output interpretation.", "findings": [ { "id": "representation-contract", "name": "Representation contract", "description": "Record how raw inputs become model inputs and how learned outputs acquire task meaning.", "questions": [ { "text": "Which tokenisation, normalisation, sampling or graph-construction steps determine what information reaches the network?", "kind": "definition", "id": "representation-contract-q01" }, { "text": "What evidence supports any claimed meaning or usefulness of an embedding, hidden state or output score?", "kind": "measurement", "id": "representation-contract-q02" } ] } ] } ] }, { "id": "data-and-learning-signals", "name": "Data and learning signals", "description": "Connect learned behaviour to training examples, feedback and partitioning decisions.", "rationale": "A deep model's behaviour depends on the examples and signals it encountered, including dependencies that evaluation can accidentally conceal.", "layers": [ { "id": "training-distribution", "name": "Training distribution", "description": "Characterise the data mixture and transformations that define training exposure.", "findings": [ { "id": "training-exposure", "name": "Training exposure", "description": "Record relevant data origins, coverage, sampling weights and augmentation choices.", "questions": [ { "text": "Which data versions, populations, modalities and sampling weights contributed to each training stage?", "kind": "provenance", "id": "training-exposure-q01" }, { "text": "How do filtering and augmentation change coverage or preserve the task-relevant meaning of examples?", "kind": "boundary", "id": "training-exposure-q02" } ] } ] }, { "id": "targets-and-partitions", "name": "Targets and partitions", "description": "Examine the source of supervision and independence of evaluation examples.", "findings": [ { "id": "signal-and-leakage-control", "name": "Signal and leakage control", "description": "Trace labels, generated targets or rewards and assess overlap across training and evaluation.", "questions": [ { "text": "How were labels, self-supervised targets, preference signals or rewards produced and checked?", "kind": "provenance", "id": "signal-and-leakage-control-q01" }, { "text": "Which duplicate, entity, temporal or benchmark overlaps could allow evaluation success without the intended generalisation?", "kind": "measurement", "id": "signal-and-leakage-control-q02" } ] } ] } ] }, { "id": "training-and-adaptation", "name": "Training and adaptation", "description": "Describe how parameters are learned, revised and selected.", "rationale": "Architecture alone does not determine behaviour; objectives, optimisation and adaptation create the model state an agent actually uses.", "layers": [ { "id": "objective-and-optimisation", "name": "Objective and optimisation", "description": "Connect the training objective to the update process and intended task.", "findings": [ { "id": "learning-procedure", "name": "Learning procedure", "description": "Record losses, update rules, schedules and evidence of training progress or instability.", "questions": [ { "text": "What objective is optimised, and how does it approximate the behaviour wanted in use?", "kind": "definition", "id": "learning-procedure-q01" }, { "text": "Which loss curves, validation results and numerical diagnostics establish whether training progressed or failed?", "kind": "measurement", "id": "learning-procedure-q02" } ] } ] }, { "id": "adaptation-and-checkpoints", "name": "Adaptation and checkpoints", "description": "Trace changes from initialisation or a source model to a selected checkpoint.", "findings": [ { "id": "parameter-change-lineage", "name": "Parameter change lineage", "description": "Distinguish parameter updates from changes made only to prompts, retrieval context or inference settings.", "questions": [ { "text": "Which source checkpoint, trainable components, adaptation data and selection rule produced this model state?", "kind": "provenance", "id": "parameter-change-lineage-q01" }, { "text": "Should the task use full fine-tuning, partial parameter adaptation or an inference-only change, given measured benefits and constraints?", "kind": "action", "id": "parameter-change-lineage-q02" } ] } ] } ] }, { "id": "capability-and-failure-evidence", "name": "Capability and failure evidence", "description": "Assess what a trained deep model can do and where the supporting evidence ends.", "rationale": "Training success and benchmark scores do not by themselves establish fitness across populations, inputs or operating conditions.", "layers": [ { "id": "generalisation-evaluation", "name": "Generalisation evaluation", "description": "Relate task metrics to independent testing, baselines and uncertainty.", "findings": [ { "id": "supported-performance", "name": "Supported performance", "description": "Record performance with its test distribution, comparison conditions and variability.", "questions": [ { "text": "Which independent test sets, task metrics and baselines support the claimed advantage?", "kind": "measurement", "id": "supported-performance-q01" }, { "text": "How much do results vary across runs, data subsets and inference settings?", "kind": "measurement", "id": "supported-performance-q02" } ] } ] }, { "id": "robustness-and-reliance", "name": "Robustness and reliance", "description": "Identify failure under changed inputs and limits on confidence or explanations.", "findings": [ { "id": "failure-envelope", "name": "Failure envelope", "description": "Record observed failure conditions and the evidence needed before relying on model outputs.", "questions": [ { "text": "Which distribution shifts, perturbations or underrepresented groups produce unacceptable degradation?", "kind": "measurement", "id": "failure-envelope-q01" }, { "text": "What calibration, abstention or human-review evidence is required before outputs can guide the intended action?", "kind": "action", "id": "failure-envelope-q02" } ] } ] } ] }, { "id": "inference-and-operational-change", "name": "Inference and operational change", "description": "Assess execution feasibility and preserve evaluation validity as models and environments change.", "rationale": "A deep model's practical behaviour depends on serving configuration, resource constraints and continued agreement with its tested conditions.", "layers": [ { "id": "execution-and-compression", "name": "Execution and compression", "description": "Connect model execution choices to resource demand and output quality.", "findings": [ { "id": "serving-tradeoffs", "name": "Serving tradeoffs", "description": "Record workload-specific effects of precision, batching, compression and other inference choices.", "questions": [ { "text": "What latency, throughput and peak memory are measured for the intended hardware, input sizes and concurrency?", "kind": "measurement", "id": "serving-tradeoffs-q01" }, { "text": "Which quantisation, pruning, distillation or batching changes meet resource limits while retaining required evaluation performance?", "kind": "action", "id": "serving-tradeoffs-q02" } ] } ] }, { "id": "monitoring-and-revalidation", "name": "Monitoring and revalidation", "description": "Define when observed changes require renewed evaluation or operational intervention.", "findings": [ { "id": "continued-fitness", "name": "Continued fitness", "description": "Link deployment monitoring and model changes to explicit revalidation and fallback conditions.", "questions": [ { "text": "Which changes in inputs, preprocessing, checkpoints or serving configuration invalidate existing evaluation evidence?", "kind": "boundary", "id": "continued-fitness-q01" }, { "text": "What observed failure or drift threshold triggers reevaluation, rollback, retraining or suspension?", "kind": "action", "id": "continued-fitness-q02" } ] } ] } ] }, { "id": "evidence-and-external-alignment", "name": "Evidence and external alignment", "description": "What the world already says about this thing, gathered so the model can be checked against it.", "rationale": "A model that cannot be lined up against existing standards, identifiers and practice cannot be adopted by anyone who already uses them.", "layers": [ { "id": "reported-evidence", "name": "Reported evidence", "description": "Findings from the breadth pass, kept separate from the structural claims.", "findings": [ { "id": "evidence-confidence-notes", "name": "Check these first", "description": "Recalled without web access and unsourced; every item is a lead to verify.", "evidence": [ "The sense described is a machine-learning methodology and research area, not a work, medium, or creative form; the supplied domain classification should be checked.", "The listed kinds overlap and mix architectural families with generative modeling approaches; they are not mutually exclusive.", "This is recall-based information; standard scope and applicability have not been checked against source texts." ], "questions": [ { "id": "evidence-confidence-notes-q01", "text": "Which of these check these first hold for the sense of deep learning this model covers, and on what evidence?", "kind": "provenance" } ] }, { "id": "evidence-kinds", "name": "Kinds and varieties", "description": "Recalled without web access and unsourced; every item is a lead to verify.", "evidence": [ "Convolutional neural networks", "Recurrent neural networks", "Transformer networks", "Deep autoencoders", "Generative adversarial networks", "Neural diffusion models" ], "questions": [ { "id": "evidence-kinds-q01", "text": "Which of these kinds and varieties hold for the sense of deep learning this model covers, and on what evidence?", "kind": "provenance" } ] }, { "id": "evidence-standards-and-regulation", "name": "Standards and regulation", "description": "Recalled without web access and unsourced; every item is a lead to verify.", "evidence": [ "ISO/IEC 22989, issued by ISO and IEC, establishes artificial-intelligence concepts and terminology relevant to deep learning.", "ISO/IEC 23894, issued by ISO and IEC, provides guidance on artificial-intelligence risk management." ], "questions": [ { "id": "evidence-standards-and-regulation-q01", "text": "Which of these standards and regulation hold for the sense of deep learning this model covers, and on what evidence?", "kind": "provenance" } ] }, { "id": "evidence-real-world-use", "name": "Real-world use", "description": "Recalled without web access and unsourced; every item is a lead to verify.", "evidence": [ "Image recognition, object detection, and image segmentation", "Speech recognition and speech synthesis", "Language translation, text generation, and information extraction", "Recommendation and ranking systems", "Scientific modeling, including prediction of molecular properties" ], "questions": [ { "id": "evidence-real-world-use-q01", "text": "Which of these real-world use hold for the sense of deep learning this model covers, and on what evidence?", "kind": "provenance" } ] }, { "id": "evidence-measurements", "name": "Typical measurements", "description": "Recalled without web access and unsourced; every item is a lead to verify.", "evidence": [ { "quantity": "Number of trainable parameters", "typical_range": "Architecture-dependent; no defining range", "unit": "parameters" }, { "quantity": "Classification accuracy", "typical_range": "0-100; meaningful comparisons require the same task and evaluation conditions", "unit": "%" }, { "quantity": "Inference latency", "typical_range": "Depends on model, hardware, input length, and batch size", "unit": "milliseconds per inference" } ], "questions": [ { "id": "evidence-measurements-q01", "text": "Which of these typical measurements hold for the sense of deep learning this model covers, and on what evidence?", "kind": "provenance" } ] }, { "id": "evidence-failure-modes-and-hazards", "name": "Failure modes and hazards", "description": "Recalled without web access and unsourced; every item is a lead to verify.", "evidence": [ "Overfitting or data leakage can produce misleading evaluation results.", "Performance can deteriorate when deployment data differ from training data.", "Training data and design choices can produce unequal error rates across populations.", "Adversarial inputs can induce incorrect predictions; some models can expose memorized sensitive training data.", "Generative models can produce plausible but false outputs." ], "questions": [ { "id": "evidence-failure-modes-and-hazards-q01", "text": "Which of these failure modes and hazards hold for the sense of deep learning this model covers, and on what evidence?", "kind": "provenance" } ] }, { "id": "evidence-regional-variation", "name": "Regional variation", "description": "Recalled without web access and unsourced; every item is a lead to verify.", "evidence": [ "Available languages, datasets, and computing resources affect which applications and populations are well supported.", "Privacy, copyright, and sector-specific requirements vary by jurisdiction and application." ], "questions": [ { "id": "evidence-regional-variation-q01", "text": "Which of these regional variation hold for the sense of deep learning this model covers, and on what evidence?", "kind": "provenance" } ] }, { "id": "evidence-neighbours", "name": "Neighbouring kinds and how to tell them apart", "description": "Recalled without web access and unsourced; every item is a lead to verify.", "evidence": [ { "name": "Machine learning", "difference": "Machine learning is the broader field and includes methods such as decision trees that do not use deep neural networks." }, { "name": "Artificial neural network", "difference": "Neural networks include shallow architectures; deep learning emphasizes learning through multiple processing layers, without a universally agreed numerical depth threshold." }, { "name": "Representation learning", "difference": "Representation learning concerns learning useful features and can use shallow or non-neural methods; deep learning learns representations through layered neural computation." }, { "name": "Generative artificial intelligence", "difference": "Generative AI is distinguished by producing content; deep learning also supports discriminative tasks, and generative methods need not be deep." } ], "questions": [ { "id": "evidence-neighbours-q01", "text": "Which of these neighbouring kinds and how to tell them apart hold for the sense of deep learning this model covers, and on what evidence?", "kind": "provenance" } ] } ] } ] } ] }, "openQuestions": [ "Which existing Vercy world model, if any, already owns deep learning, and should this registry entry link to it?", "Does the INF / INF.MED placement represent deep learning as a method, a knowledge field or another registry-specific sense?", "What operational convention should distinguish deep from shallow architectures, particularly for recurrent, unrolled and hybrid systems?", "How should responsibilities be divided among this model and neighbouring models for neural networks, foundation models and individual trained checkpoints?", "Which authoritative sources and task-specific evaluation standards should ground the researched publication and its acceptance criteria?" ], "statistics": { "bundles": 7, "layers": 13, "findings": 20, "questions": 32 } }, "draft": { "generator": "vr.draft.v3", "status": "draft-generated", "researched": false, "archetype": "work, medium or creative form", "method": "Written from the archetype playbook - what this kind of thing needs beyond identity and provenance - and from the structure that recurred across 6,333 models already researched by two engines. Applied to this entry by rule. No source was read for this thing and no claim here is researched. This entry carries no facets of its own, so they were inferred from its domain - a guess about a whole domain applied to one thing.", "facetsInferred": true, "nextPass": "A researcher replaces this draft with a sourced specification. Treat every sentence below as a proposal to argue with.", "purpose": "Give an agent a durable, checkable way to recognise a deep learning, record what state it is in, and decide what may be done with it.", "whatItIs": "Enable an AI agent to recognise deep learning approaches, assess the evidence supporting their capabilities, and determine appropriate training, adaptation, evaluation and deployment actions.", "characteristics": { "substance": "information", "origin": "conceptual", "agency": "inert" }, "whatYouCanDoWithIt": [ "read and interpreted", "observed and measured" ], "distinguishingFeatures": [ "Names folded into this entry, which a task may need to split apart again: deep transfer learning, deep reinforcement learning, topological deep learning.", "3 finer distinctions are held as aliases rather than separate entries, because telling them apart needs a task that asks for it.", "Described in 73 Wikipedia languages, which is a measure of how widely the thing is known, not of how important it is." ], "openQuestionsForResearch": [ "Which of the bundles below does a real task actually need, and which are ceremony?", "What does this thing have that the facets do not capture at all?", "Which neighbouring kind is most often confused with a deep learning, and on what evidence are they told apart?" ], "whatItIsMadeOf": "content that has to be carried by something else", "physicalCharacter": [ "Does nothing on its own; everything it does, something else did to it.", "These come from the domain this entry sits in rather than from the entry itself, so treat them as a first guess about the whole domain applied to one thing." ], "whatCanBeDoneWithIt": [ "read it and act on what it says", "observe it, measure it, record its state" ], "howItIsRecognised": [ "What is seen is a copy or a performance, not the work. Recognising the work means recognising the content through whatever is carrying it." ], "relatedModels": [ { "relation": "covers", "note": "Finer kinds folded into this entry because telling them apart needs a task that asks for it. Each is a model waiting to be split out when one does.", "targets": [ "deep transfer learning", "deep reinforcement learning", "topological deep learning" ] } ], "standing": "Described in 73 Wikipedia languages, which measures how widely it is written about rather than how important or how common it is. 3 finer distinctions are held inside this entry as names rather than as separate models.", "structure": { "bundles": [ { "id": "identity-and-classification", "name": "Identity, naming and classification", "description": "How an agent tells one deep learning from another, and a deep learning from things that resemble it.", "rationale": "Recognition comes before every other claim. Without stable identity nothing else in the model can be trusted to be about the same thing twice.", "layers": [ { "id": "naming-and-identifiers", "name": "Names and identifiers", "description": "The names this thing goes by and the identifiers that survive translation and time.", "findings": [ { "id": "preferred-name-and-aliases", "name": "Preferred name, aliases and local names", "description": "Which name to use, which names mean the same thing, and which merely sound similar.", "questions": [ { "id": "preferred-name-and-aliases-q01", "text": "What identifies and describes the name of a deep learning, and in what units or vocabulary?", "kind": "definition", "answer_data": [ "identifiers", "types and classes", "values with units", "explicit unknowns" ] }, { "id": "preferred-name-and-aliases-q02", "text": "Who or what asserted this about the name of a deep learning, by which method, and when was it true?", "kind": "provenance", "answer_data": [ "authority", "method", "evidence", "event time", "knowledge time" ] }, { "id": "preferred-name-and-aliases-q03", "text": "What may an agent decide or do once the name of a deep learning is known, and what must it refuse?", "kind": "action", "answer_data": [ "permitted actions", "preconditions", "refusals", "escalation" ] } ] }, { "id": "stable-identifiers", "name": "Stable identifiers and external keys", "description": "Identifiers that keep pointing at this kind of thing across systems and languages.", "questions": [ { "id": "stable-identifiers-q01", "text": "What identifies and describes an identifier for a deep learning, and in what units or vocabulary?", "kind": "definition", "answer_data": [ "identifiers", "types and classes", "values with units", "explicit unknowns" ] }, { "id": "stable-identifiers-q02", "text": "Who or what asserted this about an identifier for a deep learning, by which method, and when was it true?", "kind": "provenance", "answer_data": [ "authority", "method", "evidence", "event time", "knowledge time" ] }, { "id": "stable-identifiers-q03", "text": "What may an agent decide or do once an identifier for a deep learning is known, and what must it refuse?", "kind": "action", "answer_data": [ "permitted actions", "preconditions", "refusals", "escalation" ] } ] } ] }, { "id": "classification-and-granularity", "name": "Classification and granularity", "description": "Where a deep learning sits among kinds, and how finely a task needs to cut it.", "findings": [ { "id": "kind-and-parents", "name": "Kind, parents and neighbouring kinds", "description": "The classes this thing belongs to and the ones it is next to.", "questions": [ { "id": "kind-and-parents-q01", "text": "What identifies and describes the kind of a deep learning, and in what units or vocabulary?", "kind": "definition", "answer_data": [ "identifiers", "types and classes", "values with units", "explicit unknowns" ] }, { "id": "kind-and-parents-q02", "text": "Who or what asserted this about the kind of a deep learning, by which method, and when was it true?", "kind": "provenance", "answer_data": [ "authority", "method", "evidence", "event time", "knowledge time" ] }, { "id": "kind-and-parents-q03", "text": "What may an agent decide or do once the kind of a deep learning is known, and what must it refuse?", "kind": "action", "answer_data": [ "permitted actions", "preconditions", "refusals", "escalation" ] } ] }, { "id": "distinguishing-features", "name": "Distinguishing features", "description": "What separates a deep learning from the things most often confused with it.", "questions": [ { "id": "distinguishing-features-q01", "text": "What identifies and describes what distinguishes a deep learning, and in what units or vocabulary?", "kind": "definition", "answer_data": [ "identifiers", "types and classes", "values with units", "explicit unknowns" ] }, { "id": "distinguishing-features-q02", "text": "Who or what asserted this about what distinguishes a deep learning, by which method, and when was it true?", "kind": "provenance", "answer_data": [ "authority", "method", "evidence", "event time", "knowledge time" ] }, { "id": "distinguishing-features-q03", "text": "What may an agent decide or do once what distinguishes a deep learning is known, and what must it refuse?", "kind": "action", "answer_data": [ "permitted actions", "preconditions", "refusals", "escalation" ] } ] } ] } ] }, { "id": "state-and-lifecycle", "name": "State and lifecycle", "description": "The states a deep learning passes through and the events that move it between them.", "rationale": "Most decisions about a thing depend on what state it is in now, which is a claim with a time on it, not a property.", "layers": [ { "id": "lifecycle-stages", "name": "Lifecycle stages", "description": "From coming into existence to ceasing to be one of these.", "findings": [ { "id": "stages-and-transitions", "name": "Stages and transitions", "description": "The stages worth naming and what moves a deep learning between them.", "questions": [ { "id": "stages-and-transitions-q01", "text": "What identifies and describes the lifecycle of a deep learning, and in what units or vocabulary?", "kind": "definition", "answer_data": [ "identifiers", "types and classes", "values with units", "explicit unknowns" ] }, { "id": "stages-and-transitions-q02", "text": "Who or what asserted this about the lifecycle of a deep learning, by which method, and when was it true?", "kind": "provenance", "answer_data": [ "authority", "method", "evidence", "event time", "knowledge time" ] }, { "id": "stages-and-transitions-q03", "text": "What may an agent decide or do once the lifecycle of a deep learning is known, and what must it refuse?", "kind": "action", "answer_data": [ "permitted actions", "preconditions", "refusals", "escalation" ] } ] } ] }, { "id": "observations-and-status", "name": "Observations and current status", "description": "What is observed about a deep learning, how often and by whom.", "findings": [ { "id": "observation-record", "name": "Observation record", "description": "How an observation of a deep learning is recorded so that it can be superseded rather than overwritten.", "questions": [ { "id": "observation-record-q01", "text": "What identifies and describes an observation of a deep learning, and in what units or vocabulary?", "kind": "definition", "answer_data": [ "identifiers", "types and classes", "values with units", "explicit unknowns" ] }, { "id": "observation-record-q02", "text": "Who or what asserted this about an observation of a deep learning, by which method, and when was it true?", "kind": "provenance", "answer_data": [ "authority", "method", "evidence", "event time", "knowledge time" ] }, { "id": "observation-record-q03", "text": "What may an agent decide or do once an observation of a deep learning is known, and what must it refuse?", "kind": "action", "answer_data": [ "permitted actions", "preconditions", "refusals", "escalation" ] } ] } ] } ] }, { "id": "content-and-access", "name": "Content, encoding and access", "description": "What a deep learning says, how it is encoded and who may read it.", "rationale": "An informational thing carries content that can be copied, versioned and withheld, none of which its physical carrier explains.", "layers": [ { "id": "content-and-encoding", "name": "Content and encoding", "description": "The content itself, its format and its language.", "findings": [ { "id": "content-and-format", "name": "Content, format and language", "description": "What a deep learning contains and in what form it is held.", "questions": [ { "id": "content-and-format-q01", "text": "What identifies and describes the content of a deep learning, and in what units or vocabulary?", "kind": "definition", "answer_data": [ "identifiers", "types and classes", "values with units", "explicit unknowns" ] }, { "id": "content-and-format-q02", "text": "Who or what asserted this about the content of a deep learning, by which method, and when was it true?", "kind": "provenance", "answer_data": [ "authority", "method", "evidence", "event time", "knowledge time" ] }, { "id": "content-and-format-q03", "text": "What may an agent decide or do once the content of a deep learning is known, and what must it refuse?", "kind": "action", "answer_data": [ "permitted actions", "preconditions", "refusals", "escalation" ] } ] } ] }, { "id": "access-and-rights", "name": "Access, rights and retention", "description": "Who may read, copy or change it, and for how long it is kept.", "findings": [ { "id": "access-rules", "name": "Access rules and retention", "description": "The permissions attached to a deep learning and the period it survives.", "questions": [ { "id": "access-rules-q01", "text": "What identifies and describes access to a deep learning, and in what units or vocabulary?", "kind": "definition", "answer_data": [ "identifiers", "types and classes", "values with units", "explicit unknowns" ] }, { "id": "access-rules-q02", "text": "Who or what asserted this about access to a deep learning, by which method, and when was it true?", "kind": "provenance", "answer_data": [ "authority", "method", "evidence", "event time", "knowledge time" ] }, { "id": "access-rules-q03", "text": "What may an agent decide or do once access to a deep learning is known, and what must it refuse?", "kind": "action", "answer_data": [ "permitted actions", "preconditions", "refusals", "escalation" ] } ] } ] } ] }, { "id": "work-and-manifestation", "name": "Work, version and copy", "description": "Which level of deep learning a statement is about.", "rationale": "The work, the edition, the file and the copy in hand are four things; rights and content attach to different ones.", "layers": [ { "id": "levels", "name": "Levels of the thing", "description": "Work, expression, manifestation and item, in plain terms.", "findings": [ { "id": "level-rules", "name": "What belongs at each level", "description": "Which facts hold for the work and which only for one copy.", "questions": [ { "id": "level-rules-q01", "text": "For deep learning, which facts belong to the work itself, which to a version, and which to a single copy?", "kind": "boundary" }, { "id": "level-rules-q02", "text": "What identifier exists at each level, and which one is being cited?", "kind": "definition" } ] } ] }, { "id": "versions", "name": "Versions, editions and variants", "description": "How versions differ and which is canonical to whom.", "findings": [ { "id": "version-record", "name": "Versions and canonicity", "description": "Differences that matter and who calls one authoritative.", "questions": [ { "id": "version-record-q01", "text": "Which versions or editions of deep learning exist, and how do they differ substantively?", "kind": "definition" }, { "id": "version-record-q02", "text": "Who treats which version as authoritative, and for what purpose?", "kind": "provenance" } ] } ] } ] }, { "id": "authorship-and-rights", "name": "Authorship, rights and permitted use", "description": "Who made deep learning and what may be done with it.", "rationale": "Rights are jurisdictional, time-limited and attached to a level, and an agent that ignores that will copy what it may not.", "layers": [ { "id": "authorship", "name": "Authorship and contribution", "description": "Who contributed what, and in what capacity.", "findings": [ { "id": "contribution-record", "name": "Contributors and roles", "description": "The people and bodies behind it, with their roles.", "questions": [ { "id": "contribution-record-q01", "text": "Who authored or contributed to deep learning, in what roles, and how is that attested?", "kind": "provenance" }, { "id": "contribution-record-q02", "text": "Where authorship is disputed or anonymous, what is recorded instead?", "kind": "boundary" } ] } ] }, { "id": "rights", "name": "Rights and permissions", "description": "Which rights subsist, where, until when, and what they permit.", "findings": [ { "id": "rights-record", "name": "Rights, term and permitted acts", "description": "The regime in force and what it allows.", "questions": [ { "id": "rights-record-q01", "text": "What rights subsist in deep learning, in which jurisdictions, and until when?", "kind": "definition" }, { "id": "rights-record-q02", "text": "Which acts are permitted without further permission, and which must an agent refuse?", "kind": "action" } ] } ] } ] }, { "id": "provenance-and-evidence", "name": "Provenance, evidence and time", "description": "Where every claim about a deep learning came from and when it held.", "rationale": "A claim without a source and a time cannot be superseded, only overwritten, and an agent that overwrites loses the ability to explain itself.", "layers": [ { "id": "source-and-authority", "name": "Source and authority", "description": "Who said it, on what evidence, and how strongly.", "findings": [ { "id": "claim-provenance", "name": "Claim provenance and confidence", "description": "The authority behind each claim about a deep learning and how confident it is.", "questions": [ { "id": "claim-provenance-q01", "text": "What identifies and describes a claim about a deep learning, and in what units or vocabulary?", "kind": "definition", "answer_data": [ "identifiers", "types and classes", "values with units", "explicit unknowns" ] }, { "id": "claim-provenance-q02", "text": "Who or what asserted this about a claim about a deep learning, by which method, and when was it true?", "kind": "provenance", "answer_data": [ "authority", "method", "evidence", "event time", "knowledge time" ] }, { "id": "claim-provenance-q03", "text": "What may an agent decide or do once a claim about a deep learning is known, and what must it refuse?", "kind": "action", "answer_data": [ "permitted actions", "preconditions", "refusals", "escalation" ] } ] } ] }, { "id": "time-and-versions", "name": "Time, versions and supersession", "description": "When a claim was true, when it was learnt, and what replaced it.", "findings": [ { "id": "validity-and-supersession", "name": "Validity period and supersession", "description": "How an old claim about a deep learning is retired without being erased.", "questions": [ { "id": "validity-and-supersession-q01", "text": "What identifies and describes the validity of a claim about a deep learning, and in what units or vocabulary?", "kind": "definition", "answer_data": [ "identifiers", "types and classes", "values with units", "explicit unknowns" ] }, { "id": "validity-and-supersession-q02", "text": "Who or what asserted this about the validity of a claim about a deep learning, by which method, and when was it true?", "kind": "provenance", "answer_data": [ "authority", "method", "evidence", "event time", "knowledge time" ] }, { "id": "validity-and-supersession-q03", "text": "What may an agent decide or do once the validity of a claim about a deep learning is known, and what must it refuse?", "kind": "action", "answer_data": [ "permitted actions", "preconditions", "refusals", "escalation" ] } ] } ] } ] } ] }, "statistics": { "bundles": 6, "layers": 12, "findings": 14, "questions": 38 } } } }