{"data":{"sci_version": "1.0", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:33002137", "kind": "science-record", "revision": 4, "content_hash": "sha256:1fd006e0a41e93c19d7ebc127469a0bc92198eb714ff617c1ef4f4fc2a0726ec", "created_at": "2026-11-01T15:41:12Z", "updated_at": "2026-11-01T21:23:53Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "A content-based dataset recommendation system for researchers-a case study on Gene Expression Omnibus (GEO) repository.", "doi": "10.1093/database/baaa064", "pmid": "33002137", "authors": ["Braja Gopal Patra", "Kirk Roberts", "Hulin Wu"], "journal": "Database : the journal of biological databases and curation", "year": 2020, "abstract": "It is a growing trend among researchers to make their data publicly available for experimental reproducibility and data reusability. Sharing data with fellow researchers helps in increasing the visibility of the work. On the other hand, there are researchers who are inhibited by the lack of data resources. To overcome this challenge, many repositories and knowledge bases have been established to date to ease data sharing. Further, in the past two decades, there has been an exponential increase in the number of datasets added to these dataset repositories. However, most of these repositories are domain-specific, and none of them can recommend datasets to researchers/users. Naturally, it is challenging for a researcher to keep track of all the relevant repositories for potential use. Thus, a dataset recommender system that recommends datasets to a researcher based on previous publications can enhance their productivity and expedite further research. This work adopts an information retrieval (IR) paradigm for dataset recommendation. We hypothesize that two fundamental differences exist between dataset recommendation and PubMed-style biomedical IR beyond the corpus. First, instead of keywords, the query is the researcher, embodied by his or her publications. Second, to filter the relevant datasets from non-relevant ones, researchers are better represented by a set of interests, as opposed to the entire body of their research. This second approach is implemented using a non-parametric clustering technique. These clusters are used to recommend datasets for each researcher using the cosine similarity between the vector representations of publication clusters and datasets. The maximum normalized discounted cumulative gain at 10 (NDCG@10), precision at 10 (p@10) partial and p@10 strict of 0.89, 0.78 and 0.61, respectively, were obtained using the proposed method after manual evaluation by five researchers. As per the best of our knowledge, this is the first study of its kind on content-based dataset recommendation. We hope that this system will further promote data sharing, offset the researchers' workload in identifying the right dataset and increase the reusability of biomedical datasets. Database URL: http://genestudy.org/recommends/#/.", "claims": [{"id": "claim-method", "statement": "Later, normalized discounted cumulative gain (NDCG) at 10 and Precision at 10 (P@10) were calculated to evaluate different systems.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33002137", "locator": "sections[17] \"Recommendation system\" paragraph 3", "quote": "Later, normalized discounted cumulative gain (NDCG) at 10 and Precision at 10 (P@10) were calculated to evaluate different systems."}], "state": "complete"}, {"id": "claim-data-access", "statement": "For the present experiment, metadata such as title, summary, submission date and name of dataset creator(s) were collected from GEO and indexed in a database, as shown in Figure 2. We also collected the PMIDs of articles associated with each dataset.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33002137", "locator": "sections[3] \"GEO Datasets\" paragraph 6", "quote": "For the present experiment, metadata such as title, summary, submission date and name of dataset creator(s) were collected from GEO and indexed in a database, as shown in Figure 2. We also collected the PMIDs of articles associated with each dataset."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": null, "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:33002137"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "blocked", "blocked_on": "data:accession_absent", "description": "The GEO series accession resolves and its sample accessions are listed. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": [], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": [], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": ["step:obtain_source_data blocked on data:accession_absent"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:33002137", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1093/database/baaa064", "sha256": "sha256:6aab330a90fcc34b1a6bf7362953fac82af65f330b229c17f3b08fa52e651924", "bytes": 51910, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1093/database/baaa064", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC7659921", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "asset:table:pmid:33002137:1", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:bbb4b1f548effabc9e52124cf8aa486c394aaac2827fdda88faa52413641aef2", "bytes": 316, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[3]/table-wrap[1]#T1", "version": null, "note": "Statistics of datasets collected from GEO", "structure": {"pmcid": "PMC7659921", "section_index": 3, "section_title": "GEO Datasets", "row_count": 3, "column_count": 2}, "table": {"label": "Table 1.", "caption": "Statistics of datasets collected from GEO", "rows": [["Datasets", "122 222"], ["Datasets with articles", "89 533"], ["Total articles", "92 884 (Mean : 0.76, Max : 10, Unique : 61 228)"]], "row_count": 3, "column_count": 2, "numeric_cells": 3, "header_rows": 0, "locator": "section[3]/table-wrap[1]#T1"}}, {"id": "asset:table:pmid:33002137:2", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:1b30b883fa6a7e82b59422532aa09cb5219fb35c578197cc79f6ab34a63a501a", "bytes": 1367, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[13]/table-wrap[1]#T2", "version": null, "note": "Number of clusters with varying α values for proposed α based on our initial evaluation. Abbreviations: P: Proposed, a: total number of clusters, b: number of clusters which contains more than one paper, c: number of clusters which contains only one paper", "structure": {"pmcid": "PMC7659921", "section_index": 13, "section_title": "MIDR System (Separate)", "row_count": 12, "column_count": 7}, "table": {"label": "Table 2.", "caption": "Number of clusters with varying α values for proposed α based on our initial evaluation. Abbreviations: P: Proposed, a: total number of clusters, b: number of clusters which contains more than one paper, c: number of clusters which contains only one paper", "rows": [["Researcher ID (No. of papers)", "Number of clusters (a, b, c) for different α values"], ["", "α = 0.3", "α = 1.0", "α = 2.0", "α = 3.0", "α = 10.0", "α = P"], ["1 (53)", "16, 9, 7", "8, 7, 1", "6, 3, 3", "5, 2, 3", "3, 2, 1", "8, 5, 3 (1.37)"], ["2 (32)", "15, 10, 5", "10, 8, 2", "8, 6, 2", "7, 5, 2", "4, 2, 2", "8, 6, 2 (1.77)"], ["3 (48)", "15, 8, 7", "9, 4, 5", "3, 3, 0", "6, 2, 4", "2, 1, 1", "6, 3, 3 (1.44)"], ["4 (22)", "15, 4, 11", "10, 5, 5", "6, 4, 2", "5, 3, 2", "4, 1, 3", "7, 4, 3 (2.13)"], ["5 (7)", "5, 2, 3", "5, 2, 3", "4, 3, 1", "5, 2, 3", "3, 3, 0", "5, 2, 3 (3.8)"], ["6 (76)", "11, 10, 1", "6, 5, 1", "3, 3, 0", "2, 2, 0", "2, 1, 1", "5, 5, 0 (1.14)"], ["7 (13)", "7, 3, 4", "5, 4, 1", "3, 3, 0", "2, 2, 0", "2, 2, 0", "2, 2, 0 (2.77)"], ["8 (193)", "16, 15, 1", "6, 5, 1", "8, 4, 4", "5, 2, 3", "4, 1, 3", "7, 6, 1 (0.72)"], ["9 (291)", "23, 23, 0", "9, 8, 1", "6, 6, 0", "6, 3, 3", "3, 1, 2", "14, 14, 0 (0.59)"], ["10 (54)", "20, 11, 9", "12, 10, 2", "9, 8, 1", "6, 6, 0", "2, 2, 0", "9, 8, 1 (1.36)"]], "row_count": 12, "column_count": 7, "numeric_cells": 75, "header_rows": 2, "locator": "section[13]/table-wrap[1]#T2"}}, {"id": "asset:table:pmid:33002137:3", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:049a33f04e69780a3c08812022233b85184d07f6969450711b74f22bfd180150", "bytes": 1384, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[17]/table-wrap[1]#T3", "version": null, "note": "Mean IACCS and ICCS for K-Means and DPMM (with different cluster sizes as mentioned in Table 2).", "structure": {"pmcid": "PMC7659921", "section_index": 17, "section_title": "Recommendation system", "row_count": 14, "column_count": 7}, "table": {"label": "Table 3.", "caption": "Mean IACCS and ICCS for K-Means and DPMM (with different cluster sizes as mentioned in Table 2).", "rows": [["", "K-Means", ""], ["", "Clusters = 2", "Clusters = 5", "DPMM"], ["", "Words", "MeSH terms", "Words", "MeSH terms", "Words", "MeSH terms"], ["Researcher ID (No. of papers)", "IACSS, ICSS", "IACSS, ICSS", "IACSS, ICSS", "IACSS, ICSS", "IACSS, ICSS", "IACSS, ICSS"], ["1 (53)", "0.14, 0.37", "0.12, 0.56", "0.20, 0.26", "0.12, 0.27", "0.22, 0.19", "0.20, 0.32"], ["2 (32)", "0.09, 0.37", "0.09, 0.42", "0.14, 0.16", "0.14, 0.23", "0.22, 0.11", "0.11, 0.12"], ["3 (48)", "0.16, 0.43", "0.16, 0.56", "0.16, 0.25", "0.16, 0.29", "0.24, 0.22", "0.17, 0.37"], ["4 (22)", "0.12, 0.24", "0.09, 0.51", "0.21, 0.11", "0.15, 0.20", "0.20, 0.10", "0.16, 0.15"], ["5 (7)", "0.16, 0.13", "0.14, 0.19", "0.18, 0.04", "0.11, 0.08", "0.45, 0.08", "0.28, 0.13"], ["6 (76)", "0.17, 0.54", "0.19, 0.64", "0.20, 0.31", "0.22, 0.36", "0.20, 0.18", "0.23, 0.19"], ["7 (13)", "0.33, 0.14", "0.47, 0.30", "0.32, 0.17", "0.45, 0.30", "0.34, 0.14", "0.47, 0.30"], ["8 (193)", "0.10, 0.61", "0.16, 0.76", "0.17, 0.31", "0.21, 0.60", "0.17, 0.25", "0.21, 0.43"], ["9 (291)", "0.07, 0.55", "0.09, 0.64", "0.09, 0.40", "0.11, 0.54", "0.23, 0.13", "0.14, 0.19"], ["10 (54)", "0.07, 0.31", "0.15, 0.57", "0.11, 0.19", "0.23, 0.33", "0.17, 0.10", "0.20, 0.24"]], "row_count": 14, "column_count": 7, "numeric_cells": 72, "header_rows": 4, "locator": "section[17]/table-wrap[1]#T3"}}, {"id": "asset:table:pmid:33002137:4", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:c2baffa6d8634c98d2ca4a1305d482c2df6493a650aadc6f9e6d948df18bae6e", "bytes": 913, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[18]/table-wrap[1]#T4", "version": null, "note": "NDCG@10, partial and strict P@10 values of the different dataset recommendation systems based on three evaluators. Abbreviations: Partial: P; Strict: S", "structure": {"pmcid": "PMC7659921", "section_index": 18, "section_title": "Results", "row_count": 8, "column_count": 10}, "table": {"label": "Table 4.", "caption": "NDCG@10, partial and strict P@10 values of the different dataset recommendation systems based on three evaluators. Abbreviations: Partial: P; Strict: S", "rows": [["Researcher ID (No. of papers)", "Baseline", "MIDR", "MIDR (separate)"], ["NDCG@10", "P@10 (P)", "P@10 (S)", "NDCG@10", "P@10 (P)", "P@10 (S)", "NDCG@10", "P@10 (P)", "P@10 (S)"], ["1 (53)", "0.82", "0.80", "0.30", "0.92", "0.90", "0.40", "0.74", "0.56", "0.32"], ["2 (32)", "0.81", "0.76", "0.50", "0.95", "0.80", "0.70", "0.52", "0.38", "0.20"], ["3 (48)", "0.76", "0.60", "0.28", "0.80", "0.60", "0.34", "0.60", "0.25", "0.13"], ["4 (22)", "0.78", "0.48", "0.36", "0.80", "0.60", "0.60", "0.48", "0.34", "0.22"], ["5 (7)", "0.85", "0.80", "0.80", "1.00", "1.00", "1.00", "0.78", "0.70", "0.70"], ["Average", "0.80", "0.69", "0.45", "0.89", "0.78", "0.61", "0.62", "0.45", "0.31"]], "row_count": 8, "column_count": 10, "numeric_cells": 68, "header_rows": 2, "locator": "section[18]/table-wrap[1]#T4"}}], "gaps": []}, "analysis": {"state": "partial", "methods": [{"id": "method-1", "family": null, "specific": null, "label": "Normalized discounted cumulative gain at 10 (NDCG@10) and Precision at 10 (P@10)", "dsl_operations": [], "missing_operations": [], "expressible": null, "parameters": {}, "data_shapes": [], "evidence": {"score": 1.0, "band": "verbatim", "quote": "Later, normalized discounted cumulative gain (NDCG) at 10 and Precision at 10 (P@10) were calculated to evaluate different systems."}}], "targets": [], "gaps": ["method.reader_named_unmapped"]}, "capability": {"state": "complete", "requirements": [{"capability_id": "capability:computational_analysis", "label": "Computational analysis platform", "assay_families": ["meta_analysis", "machine_learning", "survival_analysis", "statistical_regression", "pca", "molecular_docking", "network_analysis", "differential_expression", "gwas"], "availability_state": "owned", "tier_unlocked": "R1", "candidate": "Automated Assays Laravel application and simulated adapters"}], "gaps": []}, "readiness": {"state": "blocked", "tier": "R4", "blocking_axis": "data", "blocked_on": "data:accession_absent", "axes": {"method": {"tier": "R4", "expressible": false, "required_operations": [], "missing_operations": [], "basis": "The reader quoted the method the paper names (Normalized discounted cumulative gain at 10 (NDCG@10) and Precision at 10 (P@10)), but the corpus's method extractor could not map it onto a DSL operation, so the method is published but not expressible."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R4", "reachable": false, "accessions": [], "resolved_accessions": [], "statements": ["For the present experiment, metadata such as title, summary, submission date and name of dataset creator(s) were collected from GEO and indexed in a database, as shown in Figure 2. We also collected the PMIDs of articles associated with each dataset."], "basis": "The paper names no accession to its source data, so the data has to come from the authors."}}, "can_run": {"dsl_operations": [], "assets": ["source:pmid:33002137", "asset:table:pmid:33002137:1", "asset:table:pmid:33002137:2", "asset:table:pmid:33002137:3", "asset:table:pmid:33002137:4"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:None"]}, "gaps": ["data:accession_absent"]}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-10-31T18:22:54Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": null}, "calls": 3, "usage": {"prompt_tokens": 34331, "completion_tokens": 1676, "cached_tokens": 11520}}, "human_critique": null, "stages": [{"id": "extraction", "state": "complete", "reader": "MiniMax-M3.1-Flash", "provider": "minimax"}], "gaps": ["review.independent_reader", "review.machine_critique", "review.human_critique"]}, "execution": {"state": "absent", "status": "not_run", "mode": null, "runs": [], "gaps": ["execution.not_run"]}, "provenance": {"entities": [{"id": "pmid:33002137", "type": "paper", "label": "pmid:33002137"}, {"id": "source:pmid:33002137", "type": "manuscript", "label": "PMID 33002137 full text"}, {"id": "data:geo:None", "type": "dataset", "label": "GEO None"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:33002137", "type": "paper", "label": "pmid:33002137"}, {"id": "source:pmid:33002137", "type": "manuscript", "label": "PMID 33002137 full text"}, {"id": "data:geo:None", "type": "dataset", "label": "GEO None"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-33002137", "type": "extraction", "used": ["source:pmid:33002137"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-None", "type": "accession_resolution", "used": ["data:geo:None"], "agent": "agent:corpus-assay-engine"}], "agents": [{"id": "agent:corpus-assay-engine", "type": "software", "label": "Corpus assay engine"}, {"id": "agent:minimax-reader", "type": "software", "label": "MiniMax literature reader"}], "source_run_id": "reading-sci-2026-11-01", "dsl_catalogue_fingerprint": "343fb30756ba6ec3", "capability_table_fingerprint": "2cb3e42f047d2ecf", "snapshot_id": "ba34b49490d9bfaf"}, "lineage": {"parent_id": "pmid:33002137", "parent_hash": "sha256:448bc8e6bc3b2f30836ad847321b0760adcb9a0303e8c4bbf6d7ae2aaa41780a", "fork_of": null, "generation": 3}, "gaps": ["execution.not_run", "method.reader_named_unmapped", "review.human_critique", "review.independent_reader", "review.machine_critique", "step:obtain_source_data blocked on data:accession_absent"]},"meta":{"api_version":"v1","automation_ladder_snapshot":"ba34b49490d9bfaf","automation_ladder_caveats":["Method extraction is rule-based and has no hand-judged accuracy sample. Evidence bands order textual support; method agreement intervals are null (n=0), never probabilities. Work-type validation does not validate methods.","Operation coverage describes only explicitly named methods. It does not prove a complete protocol, compatible parameters, successful compilation or re-execution. Unrecognised or ambiguous methods require review.","Data reachability is a textual accession or public-data statement. No dataset was downloaded or checked for access, completeness or licence.","Capabilities are planning states seeded from the dated equipment register. No bench ownership, completed quote or partner booking is inferred. Procurement counts are dependencies, not guaranteed replications or financial returns.","Tier and blocker counts are deterministic assessments of this corpus, not measured agreement. R3 is reserved for procurement; unresolved methods and data require human work (R4). The most restrictive axis wins, with capability, method, data as tie-breaks. Resolving one blocker can leave another at the same tier."],"engine":"corpus_engine","engine_provider":"rule_based_v1+lex99e1195c7d6f","run_id":"run-20261023T122449232913","source":"esearch","source_detail":"PubMed mirror biomed_pubmed (read-only)","run_status":"complete","imported_at":"2026-11-03T20:16:52+00:00","generated_at":"2026-11-04T03:32:53+00:00","total_papers":5529,"total_assays_tracked":29,"automatable_assay_families":6,"validated":true,"caveat_count":9,"blocking_caveats":5,"caveats":[{"code":"low_evidence_dominates","basis":["4,152 of 5,529 classifications (75.1%) matched no cue evidence and sit at the 0.05 floor.","34 of 230 data_only calls (14.8%) are in that group."],"claim":"Work type and confidence bands","gloss":"Most records were classified from their title and framing alone, with assay-cue coverage at zero.","detail":"Most classifications in this run rest on no matched evidence at all. At the evidence floor the work type comes from title framing and a fixed prior, not from anything in the paper, so the published work-type split should be read as a rough triage signal rather than a measured distribution. The impact is bounded but not removed: since 2026-10-23 the engine excludes floor-confidence records from the shortlist entirely and publishes them separately as held out, so the recommendation is unaffected even though the census is.","status":"unverified","severity":"high"},{"code":"runtime_is_modelled","basis":["Runtime is summed from published per-assay hour figures. Timing awaits the first executed replication.","4,407 of 5,529 records (79.7%) model to 0.00 hours because no assay was matched, which the ranking then reads as \"cheapest\".","0 of 10 shortlisted records carry a 0.00-hour estimate."],"claim":"Estimated replication runtime","gloss":"The hour figures are estimates built from assay mentions; timing awaits the first executed replication.","detail":"The hour figures are a cost model derived from the bench steps implied by each matched assay family. The first executed replication will supply measured timing. Records without an assay match currently receive a zero-hour model value.","status":"unverified","severity":"high"},{"code":"signal_score_without_citations","basis":["5,529 of 5,529 records (100.0%) have no citation count from the source metadata."],"claim":"Signal score","gloss":"The signal score uses publication-type weight because the mirror supplied zero citation counts.","detail":"The source supplied no citation counts for any record in this run, so the signal score is publication-type weight only. The citation term of the ranking is inert here, and the shortlist is ordered by publication type alone.","status":"unverified","severity":"high"},{"code":"abstract_absent","basis":["1,667 of 5,529 records (30.2%) carry no abstract at all."],"claim":"Evidence base for classification","gloss":"Some records contain a title and publication type only, defining the available classification evidence.","detail":"Where the mirror contains a title and publication types only, those fields define the classifier evidence. A methods section is required for fuller automated and human review.","status":"unverified","severity":"medium"},{"code":"classifier_agreement_measured","basis":["Published two-way decision agreement 77.7% (310 of 399 decidable records) from a stratified hand-check of 438 real records.","Three-way diagnostic agreement 73.9% (288 of 390 decidable records).","data_only agreement 75.2% (100 of 133 decidable).","48 record(s) could not be decided from title and abstract and were excluded from the denominator.","56 analysis-only records were published under another work type, so the data_only share is not an upper bound."],"claim":"Published automatable decision","gloss":"This figure has been checked against a human reading the same papers, so we know how often it is right.","detail":"The published automatable / not-automatable distribution comes from a rule-based classifier that was hand-checked against a fixed ground-truth definition. The agreement rates below are measured, not estimated. The three-way work type remains a diagnostic and is reported separately.","status":"measured","severity":"high"},{"code":"llm_comparison_not_measured","basis":["rule_based_v1+lex99e1195c7d6f produced 5,529 of 5,529 classifications.","Measured on 438 labelled records; these are RULE-BASED figures, not model results."],"claim":"Rule-based versus LLM agreement","gloss":"Every number here is the rule engine’s own. Model comparison begins when a working API credential is connected and measured.","detail":"The published two-way decision agreed with a human reading on 310 of 399 decidable records (77.7%). The diagnostic three-way label agreed on 288 of 390 decidable records (73.9%). Every percentage in this run belongs to the rule engine. Model comparison begins when a working API credential is connected and measured.","status":"not_measured","severity":"high"},{"code":"confidence_is_a_band","basis":["low band: 135 of 210 hand-judged records were correct, 64.3% [57.6, 70.5] (95% CI).","medium band: 80 of 104 hand-judged records were correct, 76.9% [68.0, 84.0] (95% CI).","high band: 73 of 76 hand-judged records were correct, 96.0% [89.0, 98.7] (95% CI).","A fitted calibrated probability was built, measured and withdrawn: it overstated held-out accuracy and reorders nothing."],"claim":"Confidence band and evidence score","gloss":"The confidence band carries a measured accuracy interval; 0.85 represents relative rank strength.","detail":"Confidence on this run is a BAND plus its measured accuracy interval. The number stored alongside each record is a monotone evidence score that orders records; 0.85 represents relative rank strength. A fitted probability was withdrawn after it overstated held-out accuracy. Threshold on the band.","status":"measured","severity":"medium"},{"code":"three_way_split_is_diagnostic","basis":["The corpus is published at the two-way decision: automatable or not-automatable. The three-way split (data_only / physical / mixed) is published as a diagnostic with its measured reliability attached, not as a claim, because the rule engine's `mixed` call cannot be defended.","The engine called 22 record(s) mixed; 10 matched the human judgement (45.5%, 95% CI 26.9-65.3).","The engine's `mixed` call is right on 10/22 hand-judged records (45.5%, 95% CI 26.9%-65.3%); a constant `physical` guess scores 36.4%.","The engine's `mixed` call does not reliably beat a constant `physical` guess on the hand-judged records it labels mixed.","The sign of that difference flips between two independent samples, so the three-way refinement is not demonstrably informative.","The mixed stratum contributes a disproportionate share of errors relative to its size.","At title-only depth the physical/mixed split collapses: a title says that work was done, not whether it was also analysed."],"claim":"The published decision is two-way","gloss":"The three-way split is shown for diagnosis only. The published split is two ways.","detail":"Count and filter the corpus on automatable versus not-automatable. The data_only / physical / mixed split remains an internal engine diagnostic because the mixed call has lower measured reliability.","status":"measured","severity":"medium"},{"code":"automatable_flag_unproven","basis":["6 of 29 assay families are marked automatable now: meta_analysis, machine_learning, survival_analysis, pca, molecular_docking, differential_expression.","The flag is a rule over assay family and work type; validation begins with the first executed replication.","It inherits the 75.2% data_only agreement rate, because the flag requires the record to be analysis-only."],"claim":"Automatable now","gloss":"This flag marks assay families the rules classify as data-only. The first executed replication will validate it.","detail":"This flag marks families the rules consider replicable from data alone. Its validity depends on the analysis-only classification and will be measured through an executed replication.","status":"unverified","severity":"medium"}],"confidence_policy":"band_with_measured_interval","confidence_contract":{"policy":"band_with_measured_interval","summary":"Confidence is reported as a BAND with a measured accuracy interval. The raw evidence score supplies monotone ordering. A fitted point estimate was withdrawn after it overstated held-out accuracy.","evidence_score":"A monotone evidence score in [0,1] built from cue mass, axis separation and text coverage. Use it to order records. Interpret 0.85 as relative rank strength.","confidence_band":"low | medium | high. Consumers threshold on this value.","band_accuracy":"Wilson 95% interval on the measured agreement of the hand-judged records in this band.","calibrated_confidence":"WITHDRAWN. Reintroduction requires held-out evidence demonstrating reliable estimates across every bin."},"band_accuracy":{"low":{"n":210,"low":0.576,"band":"low","high":0.7046,"agree":135,"accuracy":0.6429},"high":{"n":76,"low":0.8903,"band":"high","high":0.9865,"agree":73,"accuracy":0.9605},"medium":{"n":104,"low":0.6795,"band":"medium","high":0.8397,"agree":80,"accuracy":0.7692}},"provider":"rule_based_v1+lex99e1195c7d6f","lexicon_id":"99e1195c7d6f","contract_published_at":"2026-10-23T12:25:29.022232+00:00","confidence_is_band":true,"confidence_is_probability":false,"decision_taxonomy":"two_way","decidable_fraction_two_way":0.8854,"decidable_fraction_two_way_low":0.8565,"decidable_fraction_two_way_high":0.9143,"decidable_fraction_three_way":0.8586,"decidable_fraction_three_way_low":0.8287,"decidable_fraction_three_way_high":0.8884,"decidable_population":5529,"validation_sample_size":438,"three_way_cost_fraction":0.0268,"three_way_cost_records":148,"decision_counts":{"automatable":230,"not_automatable":5299},"decision_split":{"automatable":{"count":230,"share":0.04159884246699222},"not_automatable":{"count":5299,"share":0.9584011575330078}},"diagnostic_work_type_counts":{"mixed":24,"physical":5275,"data_only":230}}}