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"data_gaps": ["source:pmid:40900788"]}, "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": "9f474a4836b73afa8ecd50791626fdf2bfd267c11579cf319f49f243cbe2d90b", "source_hash": "114e644acaf2c3356b9f81d1c8ca742dcd010c8be18881388edd6a42604a5b23", "raw_response_sha256": "sha256:16511676d8bf02e82bf4afc91254129ca7f505f6fba8cea75b3cd823057e3314", "raw_response_chars": 5743}, {"provider": "minimax_alt", "model": "MiniMax-M3", "prompt_version": "full-text-reading-v1", "prompt_hash": "24ca4b1439a7308c74cbce6c70ea2bdd57ec8f54f68bf03e099b340468176b04", "source_hash": "114e644acaf2c3356b9f81d1c8ca742dcd010c8be18881388edd6a42604a5b23", "raw_response_sha256": "sha256:319301853896fc6ba5b0a13161c323a267cf0f2612314734daa738120428108b", "raw_response_chars": 35001}], "target_providers": [{"provider": "minimax", "model": "MiniMax-M3.1-Flash", "prompt_version": "replication-target-v2", "prompt_hash": "256bafec96ac5d7a768e005970a310a9966e80d1d4ec9f54e26a840e4cf018d4", "source_hash": "114e644acaf2c3356b9f81d1c8ca742dcd010c8be18881388edd6a42604a5b23", "raw_response": "{\"parent_value\":0.86,\"unit\":\"AUC\",\"evidence\":\"Furthermore, prognostic ROC curves based on the GEO validation set demonstrated that CYP1B1, GABRB2, and TNFSF15 were significantly associated with thyroid cancer diagnosis (AUC exceeding 0.86).\",\"target_result\":\"The prognostic ROC curves for CYP1B1, GABRB2, and TNFSF15 showed AUCs exceeding 0.86.\",\"number_stated\":true}", "reading": {"parent_value": 0.86, "unit": "AUC", "evidence": "Furthermore, prognostic ROC curves based on the GEO validation set demonstrated that CYP1B1, GABRB2, and TNFSF15 were significantly associated with thyroid cancer diagnosis (AUC exceeding 0.86).", "target_result": "The prognostic ROC curves for CYP1B1, GABRB2, and TNFSF15 showed AUCs exceeding 0.86.", "number_stated": true, "source_locator": "chars 961-1155 of source sha256 114e644acaf2c3356b9f81d1c8ca742dcd010c8be18881388edd6a42604a5b23"}, "recorded_at": "2026-10-31T15:34:20.607141+00:00"}, {"provider": "minimax_alt", "model": "MiniMax-M3", "prompt_version": "replication-target-v2", "prompt_hash": "b21f401947a12bbc61cc97613519dc27a487e01db50a151c35158562ffd7a759", "source_hash": "114e644acaf2c3356b9f81d1c8ca742dcd010c8be18881388edd6a42604a5b23", "raw_response": "<think>We need answer JSON only. Need interpret task: find single numerical result replication registered target headline numerical result paper states for main finding. Source abstract. Candidate headline number AUC exceeding 0.86 perhaps main numerical result. Also p <0.05, six genes, adjusted P < .05 methodology. Need determine \"headline numerical result\" likely AUC exceeding 0.86, because main finding and exact quoted contiguous. Need report value paper states not calculated. Unit paper prints: perhaps \"AUC exceeding 0.86\" means number 0.86, unit? It says unit must be unit paper prints. AUC perhaps unitless. Evidence exact clause: \"Furthermore, prognostic ROC curves based on the GEO validation set demonstrated that CYP1B1, GABRB2, and TNFSF15 were significantly associated with thyroid cancer diagnosis (AUC exceeding 0.86).\" Need quote whole clause attribution. Parent_value number 0.86, unit perhaps \"AUC\" or empty? Schema unit string. User says unit must be unit paper prints. This is dimensionless; printed unit likely \"AUC\". Could set unit \"AUC\" perhaps number value and unit AUC, but AUC itself is metric, no unit. Maybe unit should be \"AUC\" because headline number \"AUC exceeding 0.86\". Alternatively 0.86 with unit \"\". Need infer intended extraction schema likely parent_value, unit, evidence, target_result, number_stated. Registered target generic main headline numerical result. We need identify one numerical result.\n\nLet's parse wording. Abstract:\n- six overlapping genes\n- p < 0.05 high-risk poorer prognosis\n- AUC exceeding 0.86 diagnosis\nConclusion.\nThe \"headline numerical result\" likely \"AUC exceeding 0.86\", as explicit performance of main model. Single numerical result. Evidence exact contiguous quotation containing number and links target. They emphasize attribution can be attached direct. Quote whole clause. Exact source includes perhaps awkward space \"( Figure...\" irrelevant.\n\nCould number be 0.86 or perhaps 86? Paper prints 0.86, no conversion. Unit AUC? AUC dimensionless. Need target_result string explain perhaps \"Prognostic ROC curves for CYP1B1, GABRB2, and TNFSF15 had AUC exceeding 0.86 for thyroid cancer diagnosis.\" Evidence exact quote. number_stated true. Unit could be \"AUC\" to satisfy printed context. They ask \"The unit must be the unit the paper prints.\" AUC isn't technically a unit, but likely expected unit field maybe \"AUC\". Could set unit \"AUC\" since paper prints AUC. parent_value 0.86. Note qualifier \"exceeding\" means exact result isn't 0.86; paper states \"AUC exceeding 0.86.\" Yet task asks numerical result paper states, headline number only. It says value paper itself states, not calculated/converted. 0.86 is stated threshold. So parent_value 0.86.\n\nCould \"six overlapping genes\" be headline numerical result? Main finding identifies three candidate hub genes and AUC >0.86. Registered target \"headline numerical result the paper states for its main finding.\" Most likely AUC >0.86. They intentionally ask single. Source probably selected around \"AUC exceeding 0.86\". Need exact evidence perhaps full sentence. Ensure exact punctuation and apostrophe. \"Furthermore, prognostic ROC curves based on the GEO validation set demonstrated that CYP1B1, GABRB2, and TNFSF15 were significantly associated with thyroid cancer diagnosis (AUC exceeding 0.86).\" Exact contiguous.\n\nUnit perhaps \"AUC\" or \"\". Let's reason from typical benchmark. They may expect JSON with parent_value numeric and unit string. For quantities like percentage, unit \"%\" etc. Here AUC is a unitless measure. Their instruction \"The unit must be the unit the paper prints.\" If no unit printed, use \"\". They might expect \"AUC\" as label? They say \"Report the headline number only: the value ...\". Output schema separates unit. \"unit must be the unit the paper prints.\" A dimensionless AUC has no physical unit. But paper prints \"AUC\", which identifies scale, and maybe they consider unit \"AUC\". Need determine expected.\n\nCould choose unit \"AUC\" because exact paper's metric. If target_result says AUC. Evidence captures qualifier. Parent_value 0.86. \"unit\": \"AUC\" seems acceptable. But technically if asked unit, \"AUC\" isn't a unit but commonly field would record metric. They may penalize if empty? Let's examine wording likely generated extraction benchmark. \"The unit must be the unit the paper prints.\" They want avoid conversions. If source says AUC &gt;0.86, unit likely \"AUC\" or perhaps no unit. They perhaps expect \"AUC\" in unit. In these tasks, `parent_value` is number and `unit` likely metric/unit. If no unit, empty string. \"Report headline number only\" doesn't mean evidence only number; requires quote clause. We can answer.\n\nNeed ensure target_result perhaps concise string. Could mention threshold and association. It says target_result perhaps what paper states for registered target. We should not overstate exact AUC; say \"AUC exceeding 0.86\". number_stated true.\n\nPotential concern: \"registered target\" is abstract generic, no explicitly singled headline result. Could choose p &lt; 0.05 because survival prognosis is main finding? Let's assess which number is more headline. Abstract conclusion: candidate hub genes; KM high-risk poorer prognosis p&lt;0.05; ROC AUC &gt;0.86. Headline numerical performance is AUC &gt;0.86. It's largest and directly about diagnosis. Replication likely needs reproduce AUC threshold. \"single numerical result that a replication of the registered target has to reproduce.\" Could target be main finding, and perhaps \"AUC exceeding 0.86\" is result. Six genes is input count, p threshold statistical significance, adjusted p methodological. Yes.\n\nCould they expect parent_value 0.86 and unit \"AUC\". Let's formulate valid JSON no markdown. Evidence exact. target_result maybe \"The GEO validation ROC analysis reported AUC exceeding 0.86 for CYP1B1, GABRB2, and TNFSF15 in thyroid cancer diagnosis.\" This paraphrases target result but okay. Evidence exact source.\n\nNeed maybe include exact quote \"Furthermore...\" no modifications. Yes.\n\nLet's answer.</think>\n\n{\"parent_value\":0.86,\"unit\":\"AUC\",\"evidence\":\"Furthermore, prognostic ROC curves based on the GEO validation set demonstrated that CYP1B1, GABRB2, and TNFSF15 were significantly associated with thyroid cancer diagnosis (AUC exceeding 0.86).\",\"target_result\":\"The GEO validation ROC analysis reported AUC exceeding 0.86 for CYP1B1, GABRB2, and TNFSF15 in thyroid cancer diagnosis.\",\"number_stated\":true}", "reading": {"parent_value": 0.86, "unit": "AUC", "evidence": "Furthermore, prognostic ROC curves based on the GEO validation set demonstrated that CYP1B1, GABRB2, and TNFSF15 were significantly associated with thyroid cancer diagnosis (AUC exceeding 0.86).", "target_result": "The GEO validation ROC analysis reported AUC exceeding 0.86 for CYP1B1, GABRB2, and TNFSF15 in thyroid cancer diagnosis.", "number_stated": true, "source_locator": "chars 961-1155 of source sha256 114e644acaf2c3356b9f81d1c8ca742dcd010c8be18881388edd6a42604a5b23"}, "recorded_at": "2026-10-31T15:34:30.225436+00:00"}], "agreement": false, "disagreements": {"target_result": {"minimax": "CYP1B1, GABRB2, and TNFSF15 were significantly associated with thyroid cancer diagnosis (AUC exceeding 0.86).", "minimax_alt": "CYP1B1, GABRB2, and TNFSF15 were significantly associated with thyroid cancer diagnosis in the GEO validation set (AUC exceeding 0.86), and the high-risk group had a significantly poorer prognosis (p < 0.05).", "decisive": false}, "analysis_methods": {"minimax": ["network toxicology", "molecular docking", "multivariate COX regression", "Kaplan-Meier survival analysis", "ROC curve analysis", "GO enrichment analysis", "KEGG pathway enrichment analysis", "hypergeometric test", "Benjamini-Hochberg false discovery rate correction"], "minimax_alt": ["network toxicology", "Venn diagram analysis", "GO and KEGG enrichment analysis", "hypergeometric test", "Benjamini-Hochberg false discovery rate correction", "multivariate COX regression", "Kaplan-Meier survival analysis", "ROC curve analysis", "molecular docking"], "decisive": false}, "statistical_method": {"minimax": "prognostic ROC curves", "minimax_alt": "multivariate COX regression; Kaplan-Meier survival analysis; prognostic ROC curve 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:1675e66f9a6724c7fc6741810acf0f13dada8420392cdc90d1dcaf9bf4139004", "type": "sep_package", "label": "SEP v1 package for pmid:40900788"}], "activities": [{"id": "activity:conversion:pmid:40900788", "type": "conversion", "used": ["package:1675e66f9a6724c7fc6741810acf0f13dada8420392cdc90d1dcaf9bf4139004"], "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:40900788", "parent_hash": "sha256:cac9797d072276930c5aa4238bd23aba2e7b98993fefed6464ecbfcef9b0012e", "fork_of": null, "generation": 2}, "gaps": ["abstract", "asset:source:pmid:40900788 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:40900788' 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":"2026-11-04T01:47:29+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}}}