{"data":{"sci_version": "1.0", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:41584048", "kind": "science-record", "revision": 1, "content_hash": "sha256:c02dc8d7b9052e1efba5939eb5413a2b579ad5d8a950731415315f0073052f16", "created_at": "2026-11-01T10:25:54Z", "updated_at": "2026-11-01T10:25:54Z", "generator": {"name": "corpus-assay-engine", "converter": "sep_to_sci_v1", "source_package_schema": "urn:bntrg:sep:1.0.0"}, "paper": {"state": "partial", "title": "Single-cell and transcriptomic profiling reveal stemness-driven immune evasion in obstructive sleep apnea (OSA) associated lung cancer.", "doi": "10.7150/jca.126708", "pmid": "41584048", "authors": null, "journal": "Journal of Cancer", "year": 2026, "abstract": null, "claims": [{"id": "claim-1", "statement": "the headline numerical result the paper states for its main finding", "kind": "quantitative", "state": "complete", "target": {"value": 6.0, "unit": "genes", "tolerance": null, "source": "chars 1633-1836 of source sha256 91f394a118d8912a20f0a1a5ff2e9ab202282bd97f87e257fecbc4a123e6b2e0"}, "evidence": [{"asset": "source:pmid:41584048", "locator": "chars 1633-1836 of source sha256 91f394a118d8912a20f0a1a5ff2e9ab202282bd97f87e257fecbc4a123e6b2e0", "quote": "LASSO modeling identified a six-gene stemness signature (EIF5A, MELTF, SEMA3C, CPS1, TCN1, SELENOK), that consistently stratified patients into high- and low-risk groups across TCGA and GSE31210 cohorts."}]}], "gaps": ["abstract", "authors"]}, "experiment": {"state": "partial", "objective": "the headline numerical result the paper states for its main finding", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate and its interval from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The number a replication must land on, and the sentence it came from.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:41584048"], "outputs": ["the published estimate, its unit and its interval"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data behind the published result", "kind": "data-preparation", "state": "blocked", "blocked_on": "data:not_downloaded", "description": "The published result cannot be recomputed without the data the paper analysed.", "duration_hours": null, "parameters": {}, "inputs": [], "outputs": ["the source data with its provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the analysis programme", "kind": "analysis", "state": "blocked", "blocked_on": "no owned computational capability is bound to this experiment", "description": "Re-run the paper's analysis on the source data and produce the registered result. | SEP input not carried into the asset list: materials:the source data behind the published result in pmid:41584048", "duration_hours": null, "parameters": {}, "inputs": [], "outputs": ["the registered result"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": ["step:obtain_source_data blocked on data:not_downloaded", "step:run_analysis blocked on no owned computational capability is bound to this experiment", "step:run_analysis input 'materials:the source data behind the published result in pmid:41584048' does not resolve to a declared asset"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:41584048", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.7150/jca.126708", "sha256": "sha256:91f394a118d8912a20f0a1a5ff2e9ab202282bd97f87e257fecbc4a123e6b2e0", "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.7150/jca.126708", "version": null, "note": "Source read by the package builder: full_text.", "structure": {"access_state": "full_text"}}], "gaps": ["asset:source:pmid:41584048 not verified"]}, "analysis": {"state": "absent", "methods": [], "targets": [], "gaps": ["method", "targets"]}, "capability": {"state": "absent", "requirements": [], "gaps": ["capability"]}, "readiness": {"state": "partial", "tier": null, "blocking_axis": null, "blocked_on": null, "axes": {}, "can_run": {"dsl_operations": [], "assets": ["source:pmid:41584048"], "steps": ["read_published_estimate"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": ["xml"], "capabilities": [], "data_gaps": ["source:pmid:41584048"]}, "gaps": ["corpus_record_absent"]}, "review": {"state": "partial", "machine_critique": {"status": "passed", "providers": [{"provider": "minimax", "model": "MiniMax-M3.1-Flash", "prompt_version": "full-text-reading-v1", "prompt_hash": "a659164fbcef65b79bb100112c37d833df6ace27aa5fbc59915b57e0072e9fea", "source_hash": "91f394a118d8912a20f0a1a5ff2e9ab202282bd97f87e257fecbc4a123e6b2e0", "raw_response_sha256": "sha256:f5da553d0f2d5b306ca92cca29fe73bc7f787578f497efd6cf4e4afba6d8e7fa", "raw_response_chars": 5969}, {"provider": "minimax_alt", "model": "MiniMax-M3", "prompt_version": "full-text-reading-v1", "prompt_hash": "aa23957ea3269ca59027bbc06d3a15049e57bfc90ed95225b0b851976740d067", "source_hash": "91f394a118d8912a20f0a1a5ff2e9ab202282bd97f87e257fecbc4a123e6b2e0", "raw_response_sha256": "sha256:c120c349771d4d732a4f36ed2791a1fcf6ea958b05c5aedd379e6f81b94c6c9f", "raw_response_chars": 65987}], "target_providers": [{"provider": "minimax", "model": "MiniMax-M3.1-Flash", "prompt_version": "replication-target-v2", "prompt_hash": "42a9df6ee6a734f42ca736df18dd0dc857e0f58c29be58494bd432c6fceb3669", "source_hash": "91f394a118d8912a20f0a1a5ff2e9ab202282bd97f87e257fecbc4a123e6b2e0", "raw_response": "{\"parent_value\":6,\"unit\":\"genes\",\"evidence\":\"LASSO modeling identified a six-gene stemness signature (EIF5A, MELTF, SEMA3C, CPS1, TCN1, SELENOK), that consistently stratified patients into high- and low-risk groups across TCGA and GSE31210 cohorts.\",\"target_result\":\"The study identified a six-gene stemness signature for prognostic stratification.\",\"number_stated\":true}", "reading": {"parent_value": 6, "unit": "genes", "evidence": "LASSO modeling identified a six-gene stemness signature (EIF5A, MELTF, SEMA3C, CPS1, TCN1, SELENOK), that consistently stratified patients into high- and low-risk groups across TCGA and GSE31210 cohorts.", "target_result": "The study identified a six-gene stemness signature for prognostic stratification.", "number_stated": true, "source_locator": "chars 1633-1836 of source sha256 91f394a118d8912a20f0a1a5ff2e9ab202282bd97f87e257fecbc4a123e6b2e0"}, "recorded_at": "2026-10-31T15:17:05.472080+00:00"}, {"provider": "minimax_alt", "model": "MiniMax-M3", "prompt_version": "replication-target-v2", "prompt_hash": "4d693a337add1fcc1925f707b5c4bfae54f4c40f9844bbe399f7aa9bfd4c5582", "source_hash": "91f394a118d8912a20f0a1a5ff2e9ab202282bd97f87e257fecbc4a123e6b2e0", "raw_response": "<think>We need answer JSON only. Need determine single numerical result headline for registered target main finding. Source abstract says LASSO identified six-gene signature. Is headline numerical result likely six-gene stemness signature. Need exact contiguous quotation containing number and attribution whole clause. \"LASSO modeling identified a six-gene stemness signature (EIF5A, MELTF, SEMA3C, CPS1, TCN1, SELENOK), that consistently stratified patients into high- and low-risk groups across TCGA and GSE31210 cohorts.\" Number spelled six, but parent_value number 6. Evidence exact quote must contain number. Unit likely \"genes\" (paper prints six-gene; unit printed perhaps \"genes\"). They request unit must be unit paper prints. Could unit be \"genes\". Headline numerical result = six-gene stemness signature. But perhaps main finding headline number might be \"six-gene\" yes. Need output target_result string perhaps \"A six-gene stemness signature...\" no paraphrase? Evidence exact quote. number_stated true. parent_value 6. unit \"genes\". target_result can state textual result. Need ensure evidence contiguous exact quotation. Quote whole clause. We need not calculate. Let's formulate valid JSON, no markdown.\n\nQuestion wording: \"find the single numerical result that a replication of the registered target has to reproduce. Report the headline number only: the value the paper itself states for the registered target, not a value you calculated...\" Since number spelled \"six\", parent_value 6. Is that considered calculated? No numerical lexical value. Unit \"genes\". Evidence exact contiguous. \"LASSO modeling identified a six-gene stemness signature (EIF5A, MELTF, SEMA3C, CPS1, TCN1, SELENOK), that consistently stratified patients into high- and low-risk groups across TCGA and GSE31210 cohorts.\" Exact punctuation. target_result maybe \"LASSO modeling identified a six-gene stemness signature.\" Is this paraphrase in field allowed. They demand headline number only perhaps target_result likely description. Schema includes target_result perhaps concise. Could say \"six-gene stemness signature\". Unit perhaps \"genes\" and evidence says six-gene. They said report headline number only perhaps parent_value 6 and unit. target_result likely \"A six-gene stemness signature...\" We should use paper wording.\n\nNeed exact quote perhaps just sentence. Ensure no accidental alteration: source: \"LASSO modeling identified a six-gene stemness signature (EIF5A, MELTF, SEMA3C, CPS1, TCN1, SELENOK), that consistently stratified patients into high- and low-risk groups across TCGA and GSE31210 cohorts.\" Exact. Unit paper prints? \"six-gene\" gives unit genes. Good.\n\nJSON:\n{\"parent_value\":6,\"unit\":\"genes\",\"evidence\":\"...\",\"target_result\":\"LASSO modeling identified a six-gene stemness signature.\",\"number_stated\":true}\n\nCould target_result include direct quotation? \"six-gene stemness signature\" perhaps. The instruction says report headline number only perhaps target_result is intended natural result. But all schema required. Should target_result be exact source wording perhaps safest: \"LASSO modeling identified a six-gene stemness signature\". Yet if report headline number only maybe no extraneous numerical info. Evidence has names and cohorts no other numbers except TCGA/GSE31210 identifiers (contains numbers but numerical result one; okay). Could choose only exact clause containing six-gene to avoid other numbers: \"LASSO modeling identified a six-gene stemness signature (EIF5A, MELTF, SEMA3C, CPS1, TCN1, SELENOK), that consistently stratified patients into high- and low-risk groups across TCGA and GSE31210 cohorts.\" There are identifiers. Whole clause attribution is clear. Is \"that...\" needed? No, attribution directly by LASSO modeling. Could quote shorter contiguous: \"LASSO modeling identified a six-gene stemness signature (EIF5A, MELTF, SEMA3C, CPS1, TCN1, SELENOK)\" but original has comma after closing parenthesis, so exact contiguous substring without comma still is exact quote. They say quote whole clause so reader can check attribution. This does. Exact punctuation? If omit comma, quote is still contiguous exact substring. But better full sentence. Numerical identifiers don't matter.\n\nCould there be another headline numerical result: 22 leukocyte subsets, 1,000 permutations, 10-fold cross validation, six-gene. Main finding specifically signature six genes. Yes.\n\nLet's final JSON only.</think>\n\n{\"parent_value\":6,\"unit\":\"genes\",\"evidence\":\"LASSO modeling identified a six-gene stemness signature (EIF5A, MELTF, SEMA3C, CPS1, TCN1, SELENOK), that consistently stratified patients into high- and low-risk groups across TCGA and GSE31210 cohorts.\",\"target_result\":\"LASSO modeling identified a six-gene stemness signature.\",\"number_stated\":true}", "reading": {"parent_value": 6, "unit": "genes", "evidence": "LASSO modeling identified a six-gene stemness signature (EIF5A, MELTF, SEMA3C, CPS1, TCN1, SELENOK), that consistently stratified patients into high- and low-risk groups across TCGA and GSE31210 cohorts.", "target_result": "LASSO modeling identified a six-gene stemness signature.", "number_stated": true, "source_locator": "chars 1633-1836 of source sha256 91f394a118d8912a20f0a1a5ff2e9ab202282bd97f87e257fecbc4a123e6b2e0"}, "recorded_at": "2026-10-31T15:17:11.515779+00:00"}], "agreement": true, "disagreements": {"target_result": {"minimax": "A six-gene stemness signature (EIF5A, MELTF, SEMA3C, CPS1, TCN1, SELENOK) that stratified lung adenocarcinoma patients into high- and low-risk groups.", "minimax_alt": "LASSO modeling identified a six-gene stemness signature (EIF5A, MELTF, SEMA3C, CPS1, TCN1, SELENOK), that consistently stratified patients into high- and low-risk groups across TCGA and GSE31210 cohorts.", "decisive": false}, "analysis_methods": {"minimax": ["Single-cell RNA sequencing analysis", "Bulk transcriptomic analysis", "CytoTRACE", "Transcriptome-based stemness scoring", "Differential expression analysis using limma", "Univariate Cox regression", "Least absolute shrinkage and selection operator (LASSO) Cox regression with 10-fold cross-validation", "Multivariate Cox regression", "Kaplan-Meier survival analysis", "Time-dependent receiver operating characteristic curves", "CIBERSORT", "ESTIMATE algorithm", "Wilcoxon rank-sum test", "Pearson correlation", "Spearman correlation", "Log-rank test", "oncoPredict"], "minimax_alt": ["CytoTRACE", "transcriptome-based stemness scoring", "limma (v3.54.2)", "univariate Cox regression", "least absolute shrinkage and selection operator (LASSO) Cox regression with 10-fold cross-validation", "weighted-expression risk-score calculation", "median-risk-score stratification", "Kaplan-Meier survival analysis", "time-dependent receiver operating characteristic (ROC) curves", "multivariate Cox regression", "CIBERSORT", "ESTIMATE algorithm", "Wilcoxon rank-sum test", "Student's t-test", "Pearson correlation", "Spearman correlation", "log-rank test", "oncoPredict"], "decisive": false}}, "agreement_basis": {"policy": "decisive_fields", "decisive_fields": ["work_type", "number_stated", "parent_value", "unit"]}, "blinded": true}, "human_critique": {"status": "not_run", "reviewer": null, "signed_at": null, "conflict_checked": false, "commentary": null}, "stages": [{"key": "target_materials", "status": "pending", "reason": null, "evidence": null}, {"key": "independent_readings", "status": "passed", "reason": "The independent readings are incomplete or disagree; the source evidence must resolve the difference.", "evidence": {"provider_errors": []}}, {"key": "source_resolution", "status": "pending", "reason": null, "evidence": null}, {"key": "protocol_reconstruction", "status": "pending", "reason": null, "evidence": null}, {"key": "control_execution", "status": "pending", "reason": null, "evidence": null}, {"key": "control_comparison", "status": "pending", "reason": null, "evidence": null}, {"key": "publication", "status": "pending", "reason": null, "evidence": null}], "gaps": ["review.human_critique", "review.machine_critique.passed"]}, "execution": {"state": "absent", "status": "not_run", "mode": null, "runs": [], "gaps": ["execution.not_run"]}, "provenance": {"entities": [{"id": "package:d7bdb1c3b36fe2d0fd7a232910ecc7efa15cb3fe1af1c65523df3c689299795a", "type": "sep_package", "label": "SEP v1 package for pmid:41584048"}], "activities": [{"id": "activity:conversion:pmid:41584048", "type": "conversion", "used": ["package:d7bdb1c3b36fe2d0fd7a232910ecc7efa15cb3fe1af1c65523df3c689299795a"], "agent": "agent:corpus-assay-engine"}], "agents": [{"id": "agent:minimax", "type": "model", "label": "minimax"}, {"id": "agent:minimax_alt", "type": "model", "label": "minimax_alt"}, {"id": "agent:corpus-assay-engine", "type": "software", "label": "Corpus assay engine"}], "source_run_id": "run-20261023T122449232913", "dsl_catalogue_fingerprint": "343fb30756ba6ec3", "capability_table_fingerprint": "2cb3e42f047d2ecf", "snapshot_id": "ba34b49490d9bfaf"}, "lineage": {"parent_id": "0e411562-6cca-48d0-a27f-98567171b156", "parent_hash": "d7bdb1c3b36fe2d0fd7a232910ecc7efa15cb3fe1af1c65523df3c689299795a", "fork_of": null, "generation": 1}, "gaps": ["abstract", "asset:source:pmid:41584048 not verified", "authors", "capability", "corpus_record_absent", "execution.not_run", "method", "review.human_critique", "review.machine_critique.passed", "step:obtain_source_data blocked on data:not_downloaded", "step:run_analysis blocked on no owned computational capability is bound to this experiment", "step:run_analysis input 'materials:the source data behind the published result in pmid:41584048' does not resolve to a declared asset", "targets"]},"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":"2026-11-04T01:21:04+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}}}