{"data":{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:16584545", "kind": "science-record", "revision": 6, "content_hash": "sha256:5593c63cd33bf8c78e0eaf17f88da1c1fe5b1c3411c33c2c7d1ab25c820764c3", "created_at": "2026-11-01T15:40:00Z", "updated_at": "2026-11-01T21:19:34Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Exploiting the full power of temporal gene expression profiling through a new statistical test: application to the analysis of muscular dystrophy data.", "doi": "10.1186/1471-2105-7-183", "pmid": "16584545", "authors": ["Veronica Vinciotti", "Xiaohui Liu", "Rolf Turk", "Emile J de Meijer", "Peter A C 't Hoen"], "journal": "BMC bioinformatics", "year": 2006, "abstract": "BACKGROUND: The identification of biologically interesting genes in a temporal expression profiling dataset is challenging and complicated by high levels of experimental noise. Most statistical methods used in the literature do not fully exploit the temporal ordering in the dataset and are not suited to the case where temporal profiles are measured for a number of different biological conditions. We present a statistical test that makes explicit use of the temporal order in the data by fitting polynomial functions to the temporal profile of each gene and for each biological condition. A Hotelling T2-statistic is derived to detect the genes for which the parameters of these polynomials are significantly different from each other.\nRESULTS: We validate the temporal Hotelling T2-test on muscular gene expression data from four mouse strains which were profiled at different ages: dystrophin-, beta-sarcoglycan and gamma-sarcoglycan deficient mice, and wild-type mice. The first three are animal models for different muscular dystrophies. Extensive biological validation shows that the method is capable of finding genes with temporal profiles significantly different across the four strains, as well as identifying potential biomarkers for each form of the disease. The added value of the temporal test compared to an identical test which does not make use of temporal ordering is demonstrated via a simulation study, and through confirmation of the expression profiles from selected genes by quantitative PCR experiments. The proposed method maximises the detection of the biologically interesting genes, whilst minimising false detections.\nCONCLUSION: The temporal Hotelling T2-test is capable of finding relatively small and robust sets of genes that display different temporal profiles between the conditions of interest. The test is simple, it can be used on gene expression data generated from any experimental design and for any number of conditions, and it allows fast interpretation of the temporal behaviour of genes. The R code is available from V.V. The microarray data have been submitted to GEO under series GSE1574 and GSE3523.", "claims": [{"id": "claim-method", "statement": "We have applied the temporal Hotelling T2-test with second order polynomials on microarray data from wild-type mice and three mouse strains with different forms of muscular dystrophy, to identify genes with differences in temporal expression profiles between the four strains.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:16584545", "locator": "sections[7] \"Analysis of muscular dystrophy dataset\" paragraph 2", "quote": "We have applied the temporal Hotelling T2-test with second order polynomials on microarray data from wild-type mice and three mouse strains with different forms of muscular dystrophy, to identify genes with differences in temporal expression profiles between the four strains."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The microarray data have been submitted to GEO under series GSE1574 and GSE3523.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:16584545", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "The microarray data have been submitted to GEO under series GSE1574 and GSE3523."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The paper presents the temporal Hotelling T2-test, a polynomial-based statistical test for detecting genes with significantly different temporal profiles across biological conditions, and identifies the source microarray data through GEO series GSE1574 and GSE3523.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:16584545", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "We present a statistical test that makes explicit use of the temporal order in the data by fitting polynomial functions to the temporal profile of each gene and for each biological condition. A Hotelling T2-statistic is derived to detect the genes for which the parameters of these polynomials are significantly different from each other."}, {"asset": "source:pmid:16584545", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "The microarray data have been submitted to GEO under series GSE1574 and GSE3523."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "The paper presents the temporal Hotelling T2-test, a polynomial-based statistical test for detecting genes with significantly different temporal profiles across biological conditions, and identifies the source microarray data through GEO series GSE1574 and GSE3523.", "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:16584545"], "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:16584545", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/1471-2105-7-183", "sha256": "sha256:011aae0dbbbbc5e22fcc59c4e0f29fd559677363128df7f30c601dc035594ea3", "bytes": 55968, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1186/1471-2105-7-183", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC1450310", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "asset:table:pmid:16584545:1", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:ddeaa23015f7a5043f0cfabf554a98bbdda683673535abc4ddcbb5d7d8533757", "bytes": 2933, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[7]/table-wrap[1]#T1", "version": null, "note": "Biomarker genes for the different muscular dystrophies. Biomarker genes selected at the 5% confidence level from temporal Hotelling pairwise tests, whereby each strain of muscular dystrophy is tested for significant differences with each of the other strains.", "structure": {"pmcid": "PMC1450310", "section_index": 7, "section_title": "Analysis of muscular dystrophy dataset", "row_count": 31, "column_count": 9}, "table": {"label": "Table 1", "caption": "Biomarker genes for the different muscular dystrophies. Biomarker genes selected at the 5% confidence level from temporal Hotelling pairwise tests, whereby each strain of muscular dystrophy is tested for significant differences with each of the other strains.", "rows": [["Strain", "GenBank Accession", "P-values", "Symbol"], ["", "", "", ""], ["", "", "MDX vs WT", "MDX vs BSG", "MDX vs GSG", "BSG vs WT", "GSG vs WT", "BSG vs GSG", ""], ["MDX", "NM_008322", "5.12E-06", "2.56E-04", "3.45E-05", "4.52E-01", "5.80E-01", "7.19E-01", "Idh2"], ["", "AF192382", "1.05E-02", "2.20E-03", "5.09E-09", "6.03E-01", "1.30E-03", "1.55E-01", "Prnd"], ["", "NM_016861", "3.27E-06", "2.56E-04", "2.63E-04", "6.78E-01", "7.60E-01", "8.18E-01", "Pdliml"], ["", "Y13832", "3.57E-02", "3.02E-02", "2.91E-06", "2.90E-01", "1.79E-05", "2.85E-01", "Gtl2"], ["", "AJ277114", "1.63E-03", "2.68E-03", "2.91E-06", "2.30E-01", "1.36E-02", "5.12E-01", "Maged2"], ["", "NM_008517", "2.58E-04", "4.91E-04", "3.95E-03", "7.06E-01", "9.17E-01", "3.90E-01", "Lta4h"], ["", "NM_021467", "3.87E-03", "2.88E-02", "2.49E-02", "1.74E-01", "8.79E-01", "6.31E-01", "Tnnil"], ["", "NM_011812", "3.00E-02", "2.66E-02", "7.89E-04", "9.97E-01", "9.98E-01", "l.OOE+00", "FblnS"], [""], ["BSG", "NM_019446", "8.24E-01", "5.63E-03", "7.65E-01", "4.15E-02", "8.54E-01", "2.06E-02", "Barhll"], ["", "NM_010902", "4.65E-01", "1.68E-02", "6.75E-01", "2.19E-03", "4.25E-01", "6.89E-03", "Nfe212"], ["", "NM_009536", "5.30E-01", "2.76E-03", "6.46E-01", "5.09E-03", "9.70E-01", "2.06E-02", "Ywhae"], ["", "NM_020329", "7.23E-01", "3.02E-02", "7.86E-01", "3.51E-03", "7.98E-01", "2.96E-02", "Dolppl"], ["", "NM_010074", "9.57E-01", "2.92E-02", "4.37E-01", "3.04E-02", "3.45E-01", "3.19E-02", "Dpp4"], ["", "NM_016755", "9.71E-01", "4.26E-02", "9.39E-01", "4.50E-02", "9.83E-01", "3.41E-02", "Atp5j"], ["", "NM_021291", "5.10E-01", "4.25E-02", "6.93E-01", "3.25E-02", "9.54E-01", "4.74E-02", "Slc7a9"], ["", "J03783", "8.29E-01", "4.43E-02", "9.67E-01", "4.53E-02", "7.68E-01", "2.92E-02", "116"], [""], ["GSG", "NM.009538", "3.72E-01", "7.22E-01", "2.75E-12", "1.67E-01", "< 1.00E-12", "2.06E-02", "Plagll"], ["", "NM_015784", "9.26E-01", "6.22E-02", "3.06E-09", "l.OOE-02", "5.71E-09", "3.38E-02", "Postn"], ["", "NM_016762", "7.33E-02", "9.23E-01", "4.18E-10", "7.13E-01", "8.45E-04", "2.21E-03", "Matn2"], ["", "M94308", "6.28E-01", "1.49E-01", "1.97E-06", "3.32E-01", "4.69E-07", "4.18E-06", "Unknown"], ["", "M15442", "9.35E-01", "8.40E-01", "9.02E-07", "4.77E-01", "2.52E-05", "2.90E-03", "Plpl"], ["", "NM_009079", "9.10E-02", "8.54E-01", "1.30E-05", "6.10E-01", "4.80E-02", "5.89E-03", "Rpl22"], ["", "AB020886", "5.50E-01", "8.89E-01", "3.10E-04", "4.31E-01", "1.68E-02", "3.38E-02", "Akapl2"], ["", "NM_007429", "5.86E-01", "9.90E-01", "2.42E-02", "7.02E-01", "4.75E-04", "1.82E-02", "Agtr2"], ["", "NM_008252", "6.77E-01", "3.70E-01", "8.78E-03", "3.31E-01", "1.04E-02", "2.90E-03", "Hmgb2"], ["", "AL391037", "9.52E-01", "9.91E-01", "2.92E-02", "9.14E-01", "2.47E-02", "4.23E-02", "Palmd"]], "row_count": 31, "column_count": 9, "numeric_cells": 185, "header_rows": 3, "locator": "section[7]/table-wrap[1]#T1"}}], "gaps": []}, "analysis": {"state": "partial", "methods": [{"id": "method-1", "family": null, "specific": null, "label": "Temporal Hotelling T2-test with second-order polynomials", "dsl_operations": [], "missing_operations": [], "expressible": null, "parameters": {}, "data_shapes": [], "evidence": {"score": 1.0, "band": "verbatim", "quote": "We have applied the temporal Hotelling T2-test with second order polynomials on microarray data from wild-type mice and three mouse strains with different forms of muscular dystrophy, to identify genes with differences in temporal expression profiles between the four strains."}}], "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 (Temporal Hotelling T2-test with second-order polynomials), 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": ["GSE1574 and GSE3523"], "resolved_accessions": [], "statements": ["The microarray data have been submitted to GEO under series GSE1574 and GSE3523."], "basis": "The paper names GSE1574 and GSE3523, but the accession does not resolve in the repository (no reason recorded), so the data cannot be reached from here. No dataset was downloaded and no licence was read."}}, "can_run": {"dsl_operations": [], "assets": ["source:pmid:16584545", "asset:table:pmid:16584545:1"], "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": "2026-10-31T18:19:56Z", "independence": "same_provider"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 88344, "completion_tokens": 1999, "cached_tokens": 70399}}, "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:16584545", "type": "paper", "label": "pmid:16584545"}, {"id": "source:pmid:16584545", "type": "manuscript", "label": "PMID 16584545 full text"}, {"id": "data:geo:None", "type": "dataset", "label": "GEO None"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:16584545", "type": "paper", "label": "pmid:16584545"}, {"id": "source:pmid:16584545", "type": "manuscript", "label": "PMID 16584545 full text"}, {"id": "data:geo:None", "type": "dataset", "label": "GEO None"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-16584545", "type": "extraction", "used": ["source:pmid:16584545"], "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:16584545", "parent_hash": "sha256:c7c5cb795a1b8c5ff89895e4a49422326d5d8749a09c3efbfd1da593b08f2dfb", "fork_of": null, "generation": 5}, "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"], "assays": [{"role": "data_generation", "definition_id": "animal_model", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["t on muscular gene expression data from four mouse strains which were profiled at different age", "ents. Interestingly, also the Gtl2 gene (the murine homologue of the human MEG3 gene), which is", "ns observed in the time series of MDX and WT mice were also found in the qPCR experiment (Figure 4,", "icroarray data from wild-type mice and three mouse strains with different forms of muscular dys"], "order_basis": "paper_text_order:941", "id": "assay:1"}, {"role": "data_generation", "definition_id": "microarray", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["genes. The R code is available from V.V. The microarray data have been submitted to GEO under series", "ing T2-test with second order polynomials on microarray data from wild-type mice and three mouse str", "due to technical artefacts arising from the microarray technology. We did so by determining the tem", "differences in heights of the curves in the microarray and qPCR experiments. Interestingly, also th"], "order_basis": "paper_text_order:2180", "id": "assay:2"}, {"role": "data_generation", "definition_id": "qpcr", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["dent quantitative reverse-transcription PCR (qPCR) technique. Distribution of P-values. P-valu", "iddle panel). The trend was confirmed by the qPCR assay (right panel). However, in the qPCR as", "he qPCR assay (right panel). However, in the qPCR assay, an even sharper drop in expression fr", "heights of the curves in the microarray and qPCR experiments. Interestingly, also the Gtl2 ge"], "order_basis": "paper_text_order:3897", "id": "assay:3"}]},"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":"2026-11-04T00:35:35+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}}}