{"data":{"sci_version": "1.0", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40867526", "kind": "science-record", "revision": 2, "content_hash": "sha256:4a922cc7c271b01b2812989433ad9e7ff5e3511fa278c749266cb0613c3874b3", "created_at": "2026-11-01T10:25:52Z", "updated_at": "2026-11-01T14:01:47Z", "generator": {"name": "corpus-assay-engine", "converter": "sep_to_sci_v1", "source_package_schema": "urn:bntrg:sep:1.0.0"}, "paper": {"state": "partial", "title": "Development of a Serum Proteomic-Based Diagnostic Model for Lung Cancer Using Machine Learning Algorithms and Unveiling the Role of SLC16A4 in Tumor Progression and Immune Response.", "doi": "10.3390/biom15081081", "pmid": "40867526", "authors": null, "journal": "Biomolecules", "year": 2025, "abstract": null, "claims": [{"id": "claim-1", "statement": "the headline numerical result the paper states for its main finding", "kind": "qualitative", "state": "blocked", "target": {"value": null, "unit": null, "tolerance": null, "source": null}, "evidence": []}], "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:40867526"], "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:40867526", "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:40867526' does not resolve to a declared asset"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:40867526", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3390/biom15081081", "sha256": "sha256:be4c6e3e7282e7c2a1d21db616bd3450798e41385a934296fb1ae88b76988589", "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3390/biom15081081", "version": null, "note": "Source read by the package builder: full_text.", "structure": {"access_state": "full_text"}}, {"id": "asset:table:pmid:40867526:1", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:59d54e5132ec21d8bb6584d086df405f90cc11e756c9a13e877524ac75cd97f3", "bytes": 1137, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[19]/table-wrap[1]#biomolecules-15-01081-t001", "version": null, "note": "Distribution of samples in the training set, testing set, and validation set.", "structure": {"pmcid": "PMC12383841", "section_index": 19, "section_title": "3.1. Dataset", "row_count": 12, "column_count": 7}, "table": {"label": "Table 1", "caption": "Distribution of samples in the training set, testing set, and validation set.", "rows": [["Characteristics", "Training(n = 144)", "Testing(n = 36)", "Validating(n = 16)", "All(n = 196)", "p"], ["Age", ">60", "56 (42.75%)", "14 (41.18%)", "4 (25.00%)", "74 (40.88%)", "0.602"], ["", "≤60", "75 (57.25%)", "20 (58.82%)", "12 (75.00%)", "107 (59.12%)"], ["gender", "female", "49 (36.84%)", "18 (50.00%)", "10 (62.50%)", "77 (41.62%)", "0.161"], ["", "male", "84 (63.16%)", "18 (50.00%)", "6 (37.50%)", "108 (58.38%)"], ["smoke", "no", "24 (68.57%)", "11 (84.62%)", "8 (100.00%)", "43 (76.79%)", "0.242"], ["", "yes", "11 (31.43%)", "2 (15.38%)", "0", "13 (23.21%)"], ["surgery", "no", "2 (5.71%)", "1 (7.69%)", "0", "3 (5.36%)", "0.896"], ["", "yes", "33 (94.29%)", "12 (92.31%)", "8 (100.00%)", "53 (94.64%)"], ["Disease type", "NegativeControl", "42 (29.17%)", "18 (50.00%)", "8 (50.00%)", "68 (34.69%)", "NA"], ["", "Disease control", "60 (41.67%)", "0", "0", "60 (30.61%)"], ["", "Cancer", "42 (29.17%)", "18 (50.00%)", "8 (50.00%)", "68 (34.69%)"]], "row_count": 12, "column_count": 7, "numeric_cells": 54, "header_rows": 1, "locator": "section[19]/table-wrap[1]#biomolecules-15-01081-t001"}}, {"id": "asset:table:pmid:40867526:2", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:fb87e3150b8d0e632089a3980a87dda543586e3d4a5dff5ddd78b3904593f2a0", "bytes": 1231, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[24]/table-wrap[1]#biomolecules-15-01081-t002", "version": null, "note": "Correlation of SLC16A4 expression with clinical characteristics in the TCGA lung cancer dataset.", "structure": {"pmcid": "PMC12383841", "section_index": 24, "section_title": "3.6. Association of SLC16A4 with Clinical Characteristics in Lung Cancer", "row_count": 23, "column_count": 5}, "table": {"label": "Table 2", "caption": "Correlation of SLC16A4 expression with clinical characteristics in the TCGA lung cancer dataset.", "rows": [["Characteristics", "N", "Expression", "p"], ["Gender", "female", "405", "2.580 ± 1.879", "<0.001"], ["", "male", "608", "1.812 ± 2.026", ""], ["Age", ">60", "720", "2.121 ± 2.014", "0.768"], ["", "≤60", "265", "2.079 ± 1.986", ""], ["M", "M0", "753", "2.049 ± 2.023", "0.002"], ["", "M1", "32", "3.241 ± 2.021", ""], ["N", "N0", "648", "2.103 ± 1.973", "0.041"], ["", "N1", "226", "1.871 ± 2.013", ""], ["", "N2", "114", "2.530 ± 2.119", ""], ["", "N3", "7", "2.073 ± 1.375", ""], ["T", "T1", "282", "2.505 ± 1.907", "0.002"], ["", "T2", "569", "1.968 ± 1.976", ""], ["", "T3", "118", "1.911 ± 2.241", ""], ["", "T4", "41", "1.995 ± 1.992", ""], ["Stage", "Stage I", "518", "2.186 ± 1.914", "0.001"], ["", "Stage II", "283", "1.853 ± 2.053", ""], ["", "Stage III", "167", "2.141 ± 2.089", ""], ["", "Stage IV", "33", "3.246 ± 1.989", ""], ["Smoke.history", "Current", "253", "1.736 ± 1.910", "<0.001"], ["", "Never", "94", "2.877 ± 1.819", ""], ["", "Reformed (>15)", "215", "2.615 ± 2.149", ""], ["", "Reformed (≤15)", "416", "1.914 ± 1.938", ""]], "row_count": 23, "column_count": 5, "numeric_cells": 55, "header_rows": 1, "locator": "section[24]/table-wrap[1]#biomolecules-15-01081-t002"}}], "gaps": ["asset:source:pmid:40867526 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:40867526", "asset:table:pmid:40867526:1", "asset:table:pmid:40867526:2"], "steps": ["read_published_estimate"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": ["xml"], "capabilities": [], "data_gaps": ["source:pmid:40867526"]}, "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": "7ab2ee4a673c079d03da2ed379f536b0e4d08f5f929ad6d9f3b595e8cc18395c", "source_hash": "be4c6e3e7282e7c2a1d21db616bd3450798e41385a934296fb1ae88b76988589", "raw_response_sha256": "sha256:3c31856f72172dbd827f32953b51103bf24819ca785909b94eac0c68bd7e272b", "raw_response_chars": 5751}, {"provider": "minimax_alt", "model": "MiniMax-M3", "prompt_version": "full-text-reading-v1", "prompt_hash": "2738fe29cebcd083e78abe39c2c7bc0d6d7f408431a152694e281c071a678821", "source_hash": "be4c6e3e7282e7c2a1d21db616bd3450798e41385a934296fb1ae88b76988589", "raw_response_sha256": "sha256:df6f525feff5fb42ae9405a0236a045a8f0e4fdcd1520b61f5c89f1554ad4811", "raw_response_chars": 79191}], "target_providers": [{"provider": "minimax", "model": "MiniMax-M3.1-Flash", "prompt_version": "replication-target-v2", "prompt_hash": "a7c51ad19f9c32aef6ee9cf2072c30fc2712d47093aa9baecf30df17b7452208", "source_hash": "be4c6e3e7282e7c2a1d21db616bd3450798e41385a934296fb1ae88b76988589", "raw_response": "{\"parent_value\":0,\"unit\":\"\",\"evidence\":\"\",\"target_result\":\"The text states that the study developed a lung cancer diagnostic model and identified SLC16A4 as a key protein, but it does not state a single headline numerical result for the main finding.\",\"number_stated\":false}", "reading": {"parent_value": 0, "unit": "", "evidence": "", "target_result": "The text states that the study developed a lung cancer diagnostic model and identified SLC16A4 as a key protein, but it does not state a single headline numerical result for the main finding.", "number_stated": false, "source_locator": null}, "recorded_at": "2026-10-31T15:33:10.109927+00:00"}, {"provider": "minimax_alt", "model": "MiniMax-M3", "prompt_version": "replication-target-v2", "prompt_hash": "239e69b17b4ea81f8b1d52da937f9b71f309925b8d960484d5e3efba88472235", "source_hash": "be4c6e3e7282e7c2a1d21db616bd3450798e41385a934296fb1ae88b76988589", "raw_response": "<think>We need answer JSON only. Need assess source. Registered target headline numerical result main finding. Source text supplied seems abstract/methods/conclusion but no actual numerical performance result (AUC etc). Need number_stated false, parent_value 0, unit empty, evidence empty, target_result state what text does state instead. Need ensure \"headline number only\" but no number. target_result likely \"The text states that the diagnostic model showed strong diagnostic value and was validated... but it gives no numerical performance result for registered target.\" Exact source doesn't contain headline numerical result. Need not invent source. JSON schema exactly. They insist evidence exact quotation if number; false leave evidence empty. target_result says what text states instead. Could quote wording? \"The study constructed a lung cancer diagnostic machine learning model using serum proteomic data from lung cancer patients, demonstrating strong diagnostic value.\" We can state this exact sentence. Need maybe model performance results omitted. Let's formulate concise. parent_value 0, unit \"\", evidence \"\", target_result maybe \"The text states: “In conclusion, this study constructed a lung cancer diagnostic machine learning model using serum proteomic data from lung cancer patients, demonstrating strong diagnostic value.” It reports no numerical value for the main diagnostic result.\" This states source wording. Is quotation exact? Yes from source. Number 0 itself required but number_stated false. Unit empty.\n\nNeed consider whether any numerical result qualifies, e.g thresholds logFC &gt;0.3 etc. But registered target headline main finding, not methods. No model performance metric. Thus false.\n\nReturn valid JSON no markdown. Ensure curly apostrophe okay.</think>\n\n{\"parent_value\":0,\"unit\":\"\",\"evidence\":\"\",\"target_result\":\"The text states that the study constructed a lung cancer diagnostic machine learning model using serum proteomic data and “demonstrating strong diagnostic value,” but it provides no numerical result for this main finding.\",\"number_stated\":false}", "reading": {"parent_value": 0, "unit": "", "evidence": "", "target_result": "The text states that the study constructed a lung cancer diagnostic machine learning model using serum proteomic data and “demonstrating strong diagnostic value,” but it provides no numerical result for this main finding.", "number_stated": false, "source_locator": null}, "recorded_at": "2026-10-31T15:33:21.698244+00:00"}], "agreement": false, "disagreements": {"target_result": {"minimax": "unknown", "minimax_alt": "unclear", "decisive": false}, "physical_methods": {"minimax": ["functional assays", "DNA methylation validation experiments"], "minimax_alt": ["SLC16A4 overexpression functional assays assessing lung cancer cell proliferation and cellular senescence", "5-aza DNA methylation validation experiments compared with negative-control groups"], "decisive": false}, "analysis_methods": {"minimax": ["nnet", "glmnet", "svm", "XGBoost", "limma", "PCAS", "GCAS", "Spearman correlation analysis", "GSEA", "unpaired Student's t-test", "one-way analysis of variance (ANOVA)"], "minimax_alt": ["Differential plasma-protein expression analysis using the limma package", "Diagnostic model development using nnet, glmnet, svm, and XGBoost", "PCAS analysis of SLC16A4 expression in LUAD and LUSC tumor and normal tissues", "GCAS analysis of SLC16A4 expression across multiple GEO lung cancer datasets", "Spearman correlation analysis using the psych package", "Gene set enrichment analysis using the ClusterProfiler R package", "Drug-sensitivity analysis using the oncoPredict tool", "Unpaired Student’s t-test", "One-way analysis of variance"], "decisive": false}, "statistical_method": {"minimax": "", "minimax_alt": "Machine learning algorithms (nnet, glmnet, svm, and XGBoost)", "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:e4f333af584cbb44464b35ec3074880fe097540af178160649eb72af39416ab5", "type": "sep_package", "label": "SEP v1 package for pmid:40867526"}], "activities": [{"id": "activity:conversion:pmid:40867526", "type": "conversion", "used": ["package:e4f333af584cbb44464b35ec3074880fe097540af178160649eb72af39416ab5"], "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:40867526", "parent_hash": "sha256:a51bbb7ab18c69d39c07f67cd878fa5c253a4f816870666d6aa9f7cd09bba0f3", "fork_of": null, "generation": 2}, "gaps": ["abstract", "asset:source:pmid:40867526 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:40867526' 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":"2026-11-04T00:31:48+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}}}