{"data":{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:31955531", "kind": "science-record", "revision": 3, "content_hash": "sha256:904fe5a4ff4df82bfe4122c6b3a3e08c6c3d1365eac9bcb1720259a694bee39e", "created_at": "2026-11-01T17:33:19Z", "updated_at": "2026-11-01T21:22:49Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "[Identification of dynamic co-expression networks in peripheral blood of rats after middle cerebral artery occlusion].", "doi": "10.3785/j.issn.1008-9292.2019.12.01", "pmid": "31955531", "authors": ["Zongfu Pan", "Xiaoping Hu", "Yiwen Zhang", "Li Li", "Ping Huang"], "journal": "Zhejiang da xue xue bao. Yi xue ban = Journal of Zhejiang University. Medical sciences", "year": 2019, "abstract": "OBJECTIVE: To identify the time dependent profiles of gene expression and featured co-expression network modules in peripheral blood of rats after middle cerebral artery occlusion (MCAO).\nMETHODS: Microarray GSE119121 from GEO database was analyzed by R language to identify the significantly changed genes in peripheral blood at different time points (0, 1, 2, 3, 6 and 24 h) after MCAO. Gene expression patterns at different time courses were screened by STEM tools. Then, function annotation and pathway enrichment of differentially expressed genes (DEGs) were performed using the Gene Ontology (GO) database and the Kyoto Gene and Genomic Encyclopedia (KEGG) database. Depending on CEMiTool package, gene expression profile matrix was inputted into R to construct the co-expression networks and to analyze modules, and enrichment analysis was conducted to evaluate the correlation between the modules and different time points.\nRESULTS: Comparing with gene at 0 h, the numbers of DEGs in peripheral blood at different time points after MCAO were 163 (1 h), 502 (2 h), 527 (3 h), 550 (6 h), and 75 (24 h), respectively. Moreover, a total of 38 gene expression patterns were enriched, and pattern 65 and pattern 34 were specifically up-regulated or down-regulated at 2-6 h. Hp, Nos2, P2ry10, and Klf12 were representative genes of these two models. The co-expression network module analysis showed that the gene status in the early acute phase (1-6 h) was positively correlated with the Module 2. Module 3 and Module 4 was positively correlated with phase phase 1-3 h and 2-6 h, respectively. Noteworthy, Module 6 gradually changed from positive correlation (0-2 h) to negative correlation (3-24 h) with the MCAO time course, and Module 6 was mainly related to viral response and innate immune response. The hub genes of Module 6 included Mx1, Mx2, and Rtp4.\nCONCLUSIONS: Our study has identified the featured genes and dynamic co-expression network modules in peripheral blood after acute ischemic stroke, which provides a potential basis for judging the onset time of ischemic stroke.", "claims": [{"id": "claim-method", "statement": "利用GEO数据库的基因表达谱芯片GSE119121联合R语言分析MCAO造模后不同时间点（0、1、2、3、6及24 h）外周血的差异表达基因。通过STEM工具筛选不同造模时程基因表达模式。利用基因本体（GO）数据库和京都基因与基因组百科全书（KEGG）数据库对差异表达的基因进行功能注释和通路富集。在R语言环境下利用CEMiTool包对基因表达谱矩阵进行共表达网络构建及模块分析，并将模块与不同造模时间点进行富集分析。", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:31955531", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "利用GEO数据库的基因表达谱芯片GSE119121联合R语言分析MCAO造模后不同时间点（0、1、2、3、6及24 h）外周血的差异表达基因。通过STEM工具筛选不同造模时程基因表达模式。利用基因本体（GO）数据库和京都基因与基因组百科全书（KEGG）数据库对差异表达的基因进行功能注释和通路富集。在R语言环境下利用CEMiTool包对基因表达谱矩阵进行共表达网络构建及模块分析，并将模块与不同造模时间点进行富集分析。"}], "state": "complete"}, {"id": "claim-data-access", "statement": "利用GEO数据库的基因表达谱芯片GSE119121联合R语言分析MCAO造模后不同时间点（0、1、2、3、6及24 h）外周血的差异表达基因。", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:31955531", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "利用GEO数据库的基因表达谱芯片GSE119121联合R语言分析MCAO造模后不同时间点（0、1、2、3、6及24 h）外周血的差异表达基因。"}], "state": "complete"}, {"id": "claim-target-result", "statement": "利用GSE119121外周血基因表达谱分析，筛选出38种基因表达模式，并识别出与造模时间相关的动态共表达网络模块。", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:31955531", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "共有38种基因表达模式被富集，其中模式65和模式34分别在2~6 h特异上调或下调， Hp、 Nos2、 P2ry10及 Klf12为两种模式的代表性基因。"}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "利用GSE119121外周血基因表达谱分析，筛选出38种基因表达模式，并识别出与造模时间相关的动态共表达网络模块。", "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:31955531"], "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": "complete", "blocked_on": null, "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": ["data:geo:GSE119121"], "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": ["data:geo:GSE119121"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:31955531", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3785/j.issn.1008-9292.2019.12.01", "sha256": "sha256:58acaeadd46e66606b852761340cc5039c7fca8c52f14101e41d8422792682cc", "bytes": 635, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3785/j.issn.1008-9292.2019.12.01", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC10412954", "source": "ncbi_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE119121", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE119121", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE119121", "version": null, "note": "利用GEO数据库的基因表达谱芯片GSE119121联合R语言分析MCAO造模后不同时间点（0、1、2、3、6及24 h）外周血的差异表达基因。", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "利用GEO数据库的基因表达谱芯片GSE119121联合R语言分析MCAO造模后不同时间点（0、1、2、3、6及24 h）外周血的差异表达基因。", "access_statement_kind": "repository_route_statement", "sample_accessions": ["GSM3358389", "GSM3358390", "GSM3358391", "GSM3358392", "GSM3358393", "GSM3358394", "GSM3358395", "GSM3358396", "GSM3358397", "GSM3358398", "GSM3358399", "GSM3358400", "GSM3358401", "GSM3358402", "GSM3358403", "GSM3358404", "GSM3358405", "GSM3358406", "GSM3358407", "GSM3358408", "GSM3358409", "GSM3358410", "GSM3358411", "GSM3358412", "GSM3358413", "GSM3358414", "GSM3358415", "GSM3358416", "GSM3358417", "GSM3358418", "GSM3358419", "GSM3358420", "GSM3358421", "GSM3358422", "GSM3358423", "GSM3358424", "GSM3358425", "GSM3358426", "GSM3358427", "GSM3358428", "GSM3358429", "GSM3358430", "GSM3358431", "GSM3358432", "GSM3358433", "GSM3358434", "GSM3358435"], "sample_count": 47, "group_sizes": null, "group_sizes_basis": null, "platform": "6247", "taxon": "Rattus norvegicus", "title": "Expression data from blood of rats", "resolution_checked_at": "2026-11-01T17:30:42Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}], "gaps": ["asset:data:geo:GSE119121 not downloaded", "asset:data:geo:GSE119121 licence not read"]}, "analysis": {"state": "partial", "methods": [{"id": "method-1", "family": "network_analysis", "specific": "pathway_enrichment", "label": "Network / pathway analysis", "dsl_operations": ["pathway_enrich"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["edge_list"], "evidence": {"score": 0.7, "band": "medium", "quote": "pathway enrichment"}}, {"id": "method-2", "family": null, "specific": null, "label": "利用R语言分析差异表达基因，并结合STEM工具筛选表达模式、利用GO和KEGG数据库进行功能注释和通路富集，以及利用CEMiTool包进行共表达网络构建及模块分析。", "dsl_operations": [], "missing_operations": [], "expressible": null, "parameters": {}, "data_shapes": [], "evidence": {"score": 1.0, "band": "verbatim", "quote": "利用GEO数据库的基因表达谱芯片GSE119121联合R语言分析MCAO造模后不同时间点（0、1、2、3、6及24 h）外周血的差异表达基因。通过STEM工具筛选不同造模时程基因表达模式。利用基因本体（GO）数据库和京都基因与基因组百科全书（KEGG）数据库对差异表达的基因进行功能注释和通路富集。在R语言环境下利用CEMiTool包对基因表达谱矩阵进行共表达网络构建及模块分析，并将模块与不同造模时间点进行富集分析。"}}], "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": "complete", "tier": "R1", "blocking_axis": "none", "blocked_on": "nothing:replicable_now", "axes": {"method": {"tier": "R1", "expressible": true, "required_operations": ["pathway_enrich"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: pathway_enrichment."}, "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": "R1", "reachable": true, "accessions": ["GSE119121"], "resolved_accessions": ["GSE119121"], "statements": ["利用GEO数据库的基因表达谱芯片GSE119121联合R语言分析MCAO造模后不同时间点（0、1、2、3、6及24 h）外周血的差异表达基因。"], "basis": "The paper names GSE119121 in GEO数据库 and the accession resolves in the repository. No dataset was downloaded and no licence was read."}}, "can_run": {"dsl_operations": ["pathway_enrich"], "assets": ["source:pmid:31955531", "data:geo:GSE119121"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE119121"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T17:00:12Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": null}, "calls": 5, "usage": {"prompt_tokens": 2025, "completion_tokens": 1236, "cached_tokens": 256}}, "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:31955531", "type": "paper", "label": "pmid:31955531"}, {"id": "source:pmid:31955531", "type": "manuscript", "label": "PMID 31955531 full text"}, {"id": "data:geo:GSE119121", "type": "dataset", "label": "GEO GSE119121"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:31955531", "type": "paper", "label": "pmid:31955531"}, {"id": "source:pmid:31955531", "type": "manuscript", "label": "PMID 31955531 full text"}, {"id": "data:geo:GSE119121", "type": "dataset", "label": "GEO GSE119121"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-31955531", "type": "extraction", "used": ["source:pmid:31955531"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE119121", "type": "accession_resolution", "used": ["data:geo:GSE119121"], "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:31955531", "parent_hash": "sha256:a1b485b68d64e507b026c694d47ab1a3dd6407a8c938ee7c4e5798a2c7b2cb41", "fork_of": null, "generation": 2}, "gaps": ["asset:data:geo:GSE119121 licence not read", "asset:data:geo:GSE119121 not downloaded", "execution.not_run", "method.reader_named_unmapped", "review.human_critique", "review.independent_reader", "review.machine_critique"], "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": 0.834, "evidence": ["o-expression networks in peripheral blood of rats after middle cerebral artery occlusion].", "ssion network modules in peripheral blood of rats after middle cerebral artery occlusion (MCAO"], "order_basis": "paper_text_order:28", "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": 0.93, "evidence": ["e cerebral artery occlusion (MCAO). METHODS: Microarray GSE119121 from GEO database was analyzed by"], "order_basis": "paper_text_order:271", "id": "assay:2"}, {"role": "analysis", "definition_id": "network_analysis", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["pathway_enrich"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 0.7, "evidence": ["pathway enrichment"], "order_basis": "consumes_earlier_output", "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":"2026-11-04T01:48:47+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}}}