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["source:pmid:40638021"]}, "gaps": ["corpus_record_absent"]}, "review": {"state": "partial", "machine_critique": {"status": "blocked", "providers": [{"provider": "minimax", "model": "MiniMax-M3.1-Flash", "prompt_version": "full-text-reading-v1", "prompt_hash": "85e9ccfcde31ab54d5a222ab96a87446b130bdf34ae8be4702931a18eb77a47f", "source_hash": "a69280a7f06454ae1f7444a6ec315691ac0cf11293efa1cb323b7f2ce222b7e0", "raw_response_sha256": "sha256:440d6514c22d1fc2aa1209bc7674f21dd9d3f3a7d860d92ba9bf0be0c14355b4", "raw_response_chars": 5325}, {"provider": "minimax_alt", "model": "MiniMax-M3", "prompt_version": "full-text-reading-v1", "prompt_hash": "a3b53727313655f747c715f9f17b8a873b02f8871ea713ae2cf248a331830327", "source_hash": "a69280a7f06454ae1f7444a6ec315691ac0cf11293efa1cb323b7f2ce222b7e0", "raw_response_sha256": "sha256:faf00cbd0cf5ac76763b6f21528118decab6b44b5e71e182a41c75a8b54aaf6a", "raw_response_chars": 46190}], "target_providers": [{"provider": "minimax", "model": 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a69280a7f06454ae1f7444a6ec315691ac0cf11293efa1cb323b7f2ce222b7e0"}, "recorded_at": "2026-10-31T15:19:15.892965+00:00"}, {"provider": "minimax_alt", "model": "MiniMax-M3", "prompt_version": "replication-target-v2", "prompt_hash": "8b0ec927e6aa67a5cfc9dfab79c2fbd8d8b6252469d78bf3c33278d69e8c73e2", "source_hash": "a69280a7f06454ae1f7444a6ec315691ac0cf11293efa1cb323b7f2ce222b7e0", "raw_response": "<think>We need parse task carefully. Need output JSON object exact schema. Need identify single numerical result replication of registered target headline number for main finding. Need interpret source. We must only supplied source text. Need determine which numerical result qualifies headline main finding. We have paper excerpt abstract and methods/results partially. Headline numerical result likely \"17 differential genes associated with cellular senescence and disulfide death were screened.\" Or \"multifactor Cox regression model had a clinical predictive effect of 5 years > 3 years > 1 year.\" Need examine what registered target means: \"the headline numerical result the paper states for its main finding\". Need pick single numerical result headline number, value paper itself states, not calc etc. We need assess source specifics. Need maybe number 17 differential genes associated ... main finding. Or 2 LASSO regression model genes. Source abstract: \"In this study, 17 differential genes associated with cellular senescence and disulfide death were screened. Single multifactor Cox regression analysis was performed to construct a prognostic risk model for breast cancer, and the results showed that the LASSO regression model contained 2 LASSO regression model genes: ACTN2, CHD4. Combined with the clinical information, the utility of the LASSO risk score and pathological stage for the prognostic risk model for breast cancer was significantly higher than that of the other variables; in addition, our constructed multifactor Cox regression model had a clinical predictive effect of 5 years > 3 years > 1 year.\"\n\nHeadline numerical result likely 17. Need careful user prompt wording \"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, converted, averaged or reconstructed. The unit must be the unit the paper prints. The evidence must be an exact, contiguous quotation from SOURCE TEXT that contains the number; do not shorten it, do not add an ellipsis and do not paraphrase it. Attribution may be carried by the text's own wording: a number may be attached to the target by a parenthetical list, by the word 'respectively', or by words that name the target directly. When that is how the text links them, quote the whole clause so a reader can check the attribution for themselves. If the text states no number for the registered target, set number_stated to false, leave parent_value at 0, leave unit and evidence empty, and say in target_result what the text does state instead.\"\n\nWe need produce headline number. They may be evaluating specific registered target; \"REGISTERED TARGET: the headline numerical result the paper states for its main finding\" generic. Need choose most headline numeric finding. 17 differential genes vs 2 LASSO genes vs predictive time horizons. \"the headline numerical result\" could be \"17\" number screened as primary result. We should reason using natural scientific headline. Main finding prognostic model uses 17 differential genes, LASSO contains 2 genes ACTN2 CHD4. They want single numerical result replication has reproduce. Is most headline numerical result 17? Likely yes, as first key numerical in abstract. Need inspect full source text maybe missing section where \"11 Hub Genes\" found and \"421 DEGs\", 143 up 278 down. But those not main finding. Target main finding \"Predictive models constructed based on genes related to cellular senescence and disulfide death predict the prognosis of breast cancer patients.\" Numerical result for main finding likely \"17 differential genes associated with cellular senescence and disulfide death were screened\" because it quantifies target basis. Or \"2 LASSO regression model genes\" because prognostic model central? Need judge.\n\nCould be dataset benchmark task where target is \"headline numerical result\" and expects one number reported. We need not provide several. Need select one.\n\nLet's parse abstract complete:\n\"Breast cancer is a heterogeneous disease with diverse prognosis and treatment outcomes. The aim of this study was to reveal the genes related to cellular senescence and disulfide death in breast cancer and to explore their potential mechanisms and clinical applications in breast cancer.\nIn this study, we screened differential genes associated with cellular senescence and disulfide death based on publicly available data, constructed a protein-protein interaction network (PPI Network), and explored the functions of differential genes by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. A prognostic risk model was constructed in the breast cancer dataset TCGA-BRCA, and a single multifactorial Cox regression was performed to assess the effect of differential genes on prognosis based on clinical information. Gene set enrichment analysis (GSEA) and gene set variant analysis (GSVA) were performed based on the median values of key prognostic gene risk scores grouped together.\nIn this study, 17 differential genes associated with cellular senescence and disulfide death were screened. Single multifactor Cox regression analysis was performed to construct a prognostic risk model for breast cancer, and the results showed that the LASSO regression model contained 2 LASSO regression model genes: ACTN2, CHD4. Combined with the clinical information, the utility of the LASSO risk score and pathological stage for the prognostic risk model for breast cancer was significantly higher than that of the other variables; in addition, our constructed multifactor Cox regression model had a clinical predictive effect of 5 years > 3 years > 1 year.\"\n\nIf tasked single headline number, in scientific abstract these often numerical outcomes aren't headline unless one key sample size or count. There is no AUC/C-index. They might expect 17 genes (main count). Wording \"registered target\" is generic from some rubric. Main finding is \"17 differential genes associated with cellular senescence and disulfide death were screened\". Maybe target_result should state that exactly or summary. unit likely \"differential genes\" or \"genes\". Headline value 17; unit paper prints \"differential genes\". Evidence whole exact sentence. Need use JSON string.\n\nBut must decide whether 2 is headline. \"the headline numerical result the paper states for its main finding\" could most directly refer \"Single multifactor Cox regression ... LASSO regression model contained 2 LASSO regression model genes: ACTN2, CHD4.\" Since main finding is a prognostic risk model and 2 model genes. Why 17 vs 2? They call \"headline\" likely first/most central abstract finding. We need maximize. Maybe task designed to detect replication target as reported \"17 differential genes\" rather than count calculated from list. 17 is salient in abstract. They often ask similar: \"Read supplied paper text and find the single numerical result that a replication of the registered target has to reproduce.\" They want headline numeric result. Usually e.g. population, effect, sample size? Here no explicit registered metric. Could be \"17\". There is no title statistic.\n\nLet's consider output expected shape unit. If \"17 differential genes\", unit might \"differential genes\". Paper prints \"17 differential genes associated with cellular senescence and disulfide death\". It is a count; unit \"differential genes\". The prompt says \"The unit must be the unit the paper prints.\" Means if paper prints \"years\", must use years; if \"genes\", use genes. For 17 evidence, unit \"differential genes\" likely. If target 2, unit \"LASSO regression model genes\". This weird repeated wording fits exact.\n\n\"Report the headline number only\" likely parent_value only one. target_result description not multiple values. Could say \"The study screened 17 differential genes associated with cellular senescence and disulfide death.\" Evidence same. number_stated true.\n\nBut we should explore possibility false: no number for registered target because \"main finding\" is prediction of prognosis (qualitative). Yet source contains counts, so not false. They explicitly have fallback. We shouldn't false since there is number. Need deliver direct.\n\nWe have time/tokens but no need. We already wrote analysis per developer but final only JSON. Need make sure no invalid char? Fine.\n\nLet's think systematically what is a \"headline numerical result\". In abstracts, the results sentence enumerates:\n1 17 differential genes ... screened\n2 LASSO model 2 genes ACTN2 CHD4\n3 utility significantly higher (no numeric)\n4 clinical predictive effect of 5 years > 3 years > 1 year.\n\nWhich is singular and can be replicated? 17 genes screening (depends on DEG thresholds) central basis. 2 genes model central final model. \"Headline numerical result\" perhaps 2 since directly number of LASSO model genes. But target definition has no explicit \"gene count\". Need infer \"main finding\" corresponds to the title \"Cellular senescence and disulfide death-related genes as biological markers of breast cancer prognosis\", main conclusion risk models. A replication needs reproduce 17 initial genes vs 2 final model genes? Likely \"17\" because headline screening result common in abstracts, while 2 is a detail. We must pick.\n\nWe can look for clues in user phrase \"the value the paper itself states for the registered target, not a value you calculated, converted, averaged or reconstructed.\" Here 17 or 2 or 421 etc all stated. They might want avoid computing \"2\" from ACTN2, CHD4. Why explicitly mention not calculated? Because for this paper, the target might be 2 genes and someone might reconstruct it from the list. Hmm! Important. They say \"not a value you calculated, converted, averaged or reconstructed.\" General instruction but this source includes \"LASSO regression model contained 2 LASSO regression model genes: ACTN2, CHD4.\" If target registered as \"number of LASSO model genes\" could reconstruct 2 from names but they want printed 2. However registered target here is generic \"the headline numerical result ... main finding\", and printed \"17\" no reconstruction needed either. Maybe dataset intentionally generic target because \"headline numerical result\" is 17. They mention not calculated to avoid choosing 421 or 143+278 or 11 etc. \n\n\"Attribution may be carried by the text's own wording: a number may be attached to the target by a parenthetical list, by the word 'respectively', or by words that name the target directly. When that is how the text links them, quote the whole clause so a reader can check the attribution for themselves.\" This generic instruction likely assembled for dataset of varied papers and exact evidence extraction. Here counts attached by direct wording. We should quote full sentence for 17.\n\nMain finding could be \"number of differential genes\" not performance. I'd choose 17. Need target_result brief same as evidence. Unit \"differential genes\" (printed). Parent_value 17.\n\nShould we unit be \"genes\" rather than \"differential genes\"? It says \"unit must be the unit the paper prints.\" The unit paper prints directly next to number is \"differential genes\". Use \"differential genes\". Exact evidence \"In this study, 17 differential genes associated with cellular senescence and disulfide death were screened.\" Yes.\n\ntarget_result could \"The study screened 17 differential genes associated with cellular senescence and disulfide death.\" It repeats number but that's fine? \"Report headline number only\" means probably no multiple numbers but text can repeat. No need number in target_result? Schema requires. Could state \"The study screened 17 differential genes associated with cellular senescence and disulfide death.\" Good.\n\nDouble check no invalid JSON types: parent_value number 17, unit string, evidence string, target_result string, number_stated boolean true. Final only JSON no markdown per developer.</think>\n\n{\"parent_value\":17,\"unit\":\"differential genes\",\"evidence\":\"In this study, 17 differential genes associated with cellular senescence and disulfide death were screened.\",\"target_result\":\"The study screened 17 differential genes associated with cellular senescence and disulfide death.\",\"number_stated\":true}", "reading": {"parent_value": 17, "unit": "differential genes", "evidence": "In this study, 17 differential genes associated with cellular senescence and disulfide death were screened.", "target_result": "The study screened 17 differential genes associated with cellular senescence and disulfide death.", "number_stated": true, "source_locator": "chars 1127-1234 of source sha256 a69280a7f06454ae1f7444a6ec315691ac0cf11293efa1cb323b7f2ce222b7e0"}, "recorded_at": "2026-10-31T15:19:42.835396+00:00"}], "agreement": false, "disagreements": {"target_result": {"minimax": "The LASSO regression model contained 2 genes: ACTN2 and CHD4.", "minimax_alt": "Seventeen cellular-senescence- and disulfide-death-related differential genes were screened; the LASSO prognostic risk model contained 2 genes (ACTN2 and CHD4), and the multifactor Cox regression model had clinical predictive effect ordered as 5 years > 3 years > 1 year.", "decisive": false}, "analysis_methods": {"minimax": ["LASSO regression", "Single multifactorial Cox regression", "Gene Ontology enrichment analysis", "KEGG pathway enrichment analysis", "Gene set enrichment analysis (GSEA)", "Gene set variation analysis (GSVA)", "limma differential analysis", "Independent Student's T-Test", "Mann-Whitney U Test (Wilcoxon Rank Sum Test)", "Kruskal-Wallis test", "Spearman correlation analysis", "Benjamini-Hochberg p-value correction"], "minimax_alt": ["Differential gene screening based on publicly available data; the specific screening procedure was not stated", "LASSO regression", "Single multifactor Cox regression", "Protein-protein interaction (PPI) network construction", "Gene Ontology (GO) enrichment analysis", "Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis", "Gene set enrichment analysis (GSEA)", "Gene set variation analysis (GSVA)", "limma differential analysis"], "decisive": false}, "statistical_method": {"minimax": "LASSO regression", "minimax_alt": "LASSO regression and single multifactor Cox regression analysis", "decisive": true}}, "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": "blocked", "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.blocked"]}, "execution": {"state": "absent", "status": "not_run", "mode": null, "runs": [], "gaps": ["execution.not_run"]}, "provenance": {"entities": [{"id": "package:b2944d1ba48d11c133211abd9a7ab4afbd85e08747f71c167cbe8ce83580bfb1", "type": "sep_package", "label": "SEP v1 package for pmid:40638021"}], "activities": [{"id": "activity:conversion:pmid:40638021", "type": "conversion", "used": ["package:b2944d1ba48d11c133211abd9a7ab4afbd85e08747f71c167cbe8ce83580bfb1"], "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": "pmid:40638021", "parent_hash": "sha256:3cf65222b2c26768baf8aa15f23d58ec0ffa8cad9c96d1851ba18d687f7234c3", "fork_of": null, "generation": 2}, "gaps": ["abstract", "asset:source:pmid:40638021 not verified", "authors", "capability", "corpus_record_absent", "execution.not_run", "method", "review.human_critique", "review.machine_critique.blocked", "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:40638021' 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":"2026-11-04T03:38:14+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}}}