{"data":{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40842541", "kind": "science-record", "revision": 4, "content_hash": "sha256:42a1f9f706eadd2ecd08479b4a834777af2bffacc44ff494c6fd8ff8840cd85e", "created_at": "2026-11-01T20:30:56Z", "updated_at": "2026-11-01T22:57:03Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Shapley additive explanations based feature selection reveals CXCL14 as a key immune-related gene in predicting idiopathic pulmonary fibrosis.", "doi": "10.3389/fmed.2025.1608078", "pmid": "40842541", "authors": ["Bin Chen", "Lu Huan", "Junyu Lu", "Jinhe Yuan"], "journal": "Frontiers in medicine", "year": 2025, "abstract": "Background: Idiopathic pulmonary fibrosis (IPF) is a progressive lung disease marked by excessive fibrous tissue accumulation in the lung interstitium, leading to a gradual deterioration of respiratory function and significantly impairing patients' quality of life. Despite advances in understanding its etiology and pathogenesis, the exact mechanisms remain unclear, underscoring the need for novel biomarkers and therapeutic targets.\nMethods: We analyzed five publicly available datasets from the Gene Expression Omnibus (GEO), specifically \"GSE15197,\" \"GSE53845,\" \"GSE135065,\" \"GSE185691,\" and \"GSE195770,\" to identify gene expression changes associated with IPF. Data were annotated and normalized to minimize batch effects and technical variability. Principal Component Analysis (PCA) verified preprocessing efficacy. Differentially expressed genes (DEGs) were identified using linear modeling. Core DEGs were selected via integrative analysis across datasets.\nResults: Our analysis revealed DEGs that are substantially linked to crucial biological processes such as extracellular matrix organization and immune response regulation. Integrative analysis of five GEO datasets identified CXCL14, MMP7, and MDK as core differentially expressed genes in the final predictive model. Using Least Absolute Shrinkage and Selection Operator (LASSO) regression and Random Forest, we constructed a logistic regression model with robust predictive performance, achieving an AUC of 0.92 in the training cohort and 0.89 in the validation cohort, with sensitivity of 88% and specificity of 85%. The Shapley Additive Explanations (SHAP) method identified CXCL14 (mean SHAP value = 0.38) as the most influential feature, followed by MMP7 and MDK. Functional enrichment analyses highlighted significant enrichment of TGF-β signaling, extracellular matrix organization, and chemokine signaling pathways. Immune infiltration analysis revealed positive correlations between CXCL14 expression and alveolar macrophage/activated fibroblast populations, while SHAP interaction analysis identified synergistic effects between CXCL14 and TGF-β1 in driving fibrosis.\nConclusion: These findings substantiate the hypothesis that IPF pathogenesis is closely linked to extracellular matrix remodeling and immune dysregulation. This suggests that future investigations should delve deeper into the practical applications of identified biomarkers in the early diagnosis and management of IPF. Furthermore, the machine learning-based predictive model demonstrates strong clinical potential and merits further validation in prospective trials to assess its utility and therapeutic implications in real-world settings.", "claims": [{"id": "claim-method", "statement": "Differential gene expression was performed using the limma package (19), which calculates gene expression differences between different groups (IPF group vs. normal control group) through linear modeling.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40842541", "locator": "sections[4] \"Differential gene expression analysis\" paragraph 2", "quote": "Differential gene expression was performed using the limma package (19), which calculates gene expression differences between different groups (IPF group vs. normal control group) through linear modeling."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40842541", "locator": "sections[22] \"Data availability statement\" paragraph 2", "quote": "The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Integrative analysis of five GEO datasets identified CXCL14, MMP7, and MDK as core differentially expressed genes, and the LASSO- and random-forest-based logistic regression model achieved an AUC of 0.92 in the training cohort and 0.89 in the validation cohort.", "kind": "quantitative", "target": {"value": 0.92, "unit": "auc", "tolerance": null, "source": "Integrative analysis of five GEO datasets identified CXCL14, MMP7, and MDK as core differentially expressed genes in the final predictive model. Using Least Absolute Shrinkage and Selection Operator (LASSO) regression and Random Forest, we constructed a logistic regression model with robust predictive performance, achieving an AUC of 0.92 in the training cohort and 0.89 in the validation cohort, with sensitivity of 88% and specificity of 85%."}, "evidence": [{"asset": "source:pmid:40842541", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Integrative analysis of five GEO datasets identified CXCL14, MMP7, and MDK as core differentially expressed genes in the final predictive model. Using Least Absolute Shrinkage and Selection Operator (LASSO) regression and Random Forest, we constructed a logistic regression model with robust predictive performance, achieving an AUC of 0.92 in the training cohort and 0.89 in the validation cohort, with sensitivity of 88% and specificity of 85%."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Integrative analysis of five GEO datasets identified CXCL14, MMP7, and MDK as core differentially expressed genes, and the LASSO- and random-forest-based logistic regression model achieved an AUC of 0.92 in the training cohort and 0.89 in the validation cohort.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:40842541"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE135065"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE135065"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:40842541", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fmed.2025.1608078", "sha256": 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directed to the corresponding author.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM3984766", "GSM3984767", "GSM3984768", "GSM3984769", "GSM3984770", "GSM3984771", "GSM3984772", "GSM3984773", "GSM3984774", "GSM3984775", "GSM3984776", "GSM3984777", "GSM3984778", "GSM3984779", "GSM3984780", "GSM3984781", "GSM3984782", "GSM3984783"], "sample_count": 18, "group_sizes": null, "group_sizes_basis": null, "platform": "15207", "taxon": "Homo sapiens", "title": "Transforming growth factor beta 1 induces methylation changes in lung fibroblasts [Affymetrix]", "resolution_checked_at": "2026-11-01T20:16:08Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}, {"id": "data:geo:GSE15197", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE15197", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE15197", "version": null, "note": "The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM379316", "GSM379317", "GSM379318", "GSM379319", "GSM379320", "GSM379321", "GSM379322", "GSM379323", "GSM379324", "GSM379325", "GSM379326", "GSM379327", "GSM379328", "GSM379329", "GSM379330", "GSM379331", "GSM379332", "GSM379333", "GSM379334", "GSM379335", "GSM379336", "GSM379337", "GSM379338", "GSM379339", "GSM379340", "GSM379341", "GSM379342", "GSM379343", "GSM379344", "GSM379345", "GSM379346", "GSM379347", "GSM379348", "GSM379349", "GSM379350", "GSM379351", "GSM379352", "GSM379353", "GSM379354"], "sample_count": 39, "group_sizes": null, "group_sizes_basis": null, "platform": "6480", "taxon": "Homo sapiens", "title": "RNA Expression Profiling of Lung Tissue Identifies Mutually Distinct Molecular Signatures in PAH and PH Secondary to IPF", "resolution_checked_at": "2026-11-01T15:39:54Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}, {"id": "data:geo:GSE185691", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": 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"group_sizes_basis": null, "platform": "20844", "taxon": "Homo sapiens", "title": "Differential Lysotracker Uptake Defines Two Populations of Distal Epithelial Cells in Idiopathic Pulmonary Fibrosis", "resolution_checked_at": "2026-11-01T20:16:08Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}, {"id": "data:geo:GSE195770", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE195770", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE195770", "version": null, "note": "The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM5849919", "GSM5849920", "GSM5849921", "GSM5849922", "GSM5849923", "GSM5849924", "GSM5849925", "GSM5849926"], "sample_count": 8, "group_sizes": null, "group_sizes_basis": null, "platform": "20844", "taxon": "Homo sapiens", "title": "Idiopathic pulmonary fibrosis (IPF)", "resolution_checked_at": "2026-11-01T20:16:08Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}, {"id": "data:geo:GSE53845", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE53845", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE53845", "version": null, "note": "The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM1302033", "GSM1302034", "GSM1302035", "GSM1302036", "GSM1302037", "GSM1302038", "GSM1302039", "GSM1302040", "GSM1302041", "GSM1302042", "GSM1302043", "GSM1302044", "GSM1302045", "GSM1302046", "GSM1302047", "GSM1302048", "GSM1302049", "GSM1302050", "GSM1302051", "GSM1302052", "GSM1302053", "GSM1302054", "GSM1302055", "GSM1302056", "GSM1302057", "GSM1302058", "GSM1302059", "GSM1302060", "GSM1302061", "GSM1302062", "GSM1302063", "GSM1302064", "GSM1302065", "GSM1302066", "GSM1302067", "GSM1302068", "GSM1302069", "GSM1302070", "GSM1302071", "GSM1302072", "GSM1302073", "GSM1302074", "GSM1302075", "GSM1302076", "GSM1302077", "GSM1302078", "GSM1302079", "GSM1302080"], "sample_count": 48, "group_sizes": null, "group_sizes_basis": null, "platform": "6480", "taxon": "Homo sapiens", "title": "40 IPF patients and 8 healthy controls", "resolution_checked_at": "2026-11-01T15:39:54Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}], "gaps": ["asset:data:geo:GSE135065 not downloaded", "asset:data:geo:GSE135065 licence not read", "asset:data:geo:GSE15197 not downloaded", "asset:data:geo:GSE15197 licence not read", "asset:data:geo:GSE185691 not downloaded", "asset:data:geo:GSE185691 licence not read", "asset:data:geo:GSE195770 not downloaded", "asset:data:geo:GSE195770 licence not read", "asset:data:geo:GSE53845 not downloaded", "asset:data:geo:GSE53845 licence not read"]}, "analysis": {"state": "partial", "methods": [{"id": "method-1", "family": "differential_expression", "specific": "limma_voom", "label": "Differential expression analysis", "dsl_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["count_matrix_with_condition"], "evidence": {"score": 1.0, "band": "verbatim", "quote": "Differential gene expression was performed using the limma package (19), which calculates gene expression differences between different groups (IPF group vs. normal control group) through linear modeling."}}, {"id": "method-2", "family": "principal_component_analysis", "specific": "pca", "label": "Principal component analysis", "dsl_operations": ["pca_fit"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["wide_numeric_matrix"], "evidence": {"score": 0.7, "band": "medium", "quote": "Principal Component Analysis"}}, {"id": "method-3", "family": "machine_learning", "specific": "random_forest", "label": "Machine learning", "dsl_operations": [], "missing_operations": ["machine_learning_random_forest"], "expressible": false, "parameters": {"auc": "rformance, achieving an AUC of 0"}, "data_shapes": ["labelled_feature_matrix"], "evidence": {"score": 0.7, "band": "medium", "quote": "Random Forest"}}, {"id": "method-4", "family": "regression_modelling", "specific": "logistic", "label": "Regression modelling", "dsl_operations": ["glm_logit_fit"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["predictors_and_outcome"], "evidence": {"score": 0.7, "band": "medium", "quote": "logistic regression"}}, {"id": "method-5", "family": "network_analysis", "specific": "pathway_enrichment", "label": "Network / pathway analysis", "dsl_operations": ["pathway_enrich"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["edge_list"], "evidence": {"score": 0.8, "band": "high", "quote": "GO enrichment"}}], "targets": [{"id": "target-published-estimate", "claim_id": "claim-target-result", "operation": "differential_expression_fit", "expected_value": 0.92, "unit": "auc", "tolerance": null, "state": "complete", "note": "The published estimate as the reader quoted it from the paper. No tolerance is published because the paper states none."}], "gaps": ["method.reader_differs_from_extractor"]}, "capability": {"state": "complete", "requirements": [{"capability_id": "capability:computational_analysis", "label": "Computational analysis platform", "assay_families": ["meta_analysis", "machine_learning", "survival_analysis", "statistical_regression", "pca", "molecular_docking", "network_analysis", "differential_expression", "gwas"], "availability_state": "owned", "tier_unlocked": "R1", "candidate": "Automated Assays Laravel application and simulated adapters"}], "gaps": []}, "readiness": {"state": "complete", "tier": "R1", "blocking_axis": "none", "blocked_on": "nothing:replicable_now", "axes": {"method": {"tier": "R1", "expressible": true, "required_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: limma_voom."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R1", "reachable": true, "accessions": ["GSE15197", "GSE53845", "GSE135065", "GSE185691", "GSE195770"], "resolved_accessions": ["GSE15197", "GSE53845", "GSE135065", "GSE185691", "GSE195770"], "statements": ["The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author."], "basis": "The paper names GSE15197, GSE53845, GSE135065, GSE185691, GSE195770 in Gene Expression Omnibus (GEO) and the accession resolves in the repository. No dataset was downloaded and no licence was read."}}, "can_run": {"dsl_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "assets": ["source:pmid:40842541", "data:geo:GSE135065"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE135065"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T20:00:07Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 13575, "completion_tokens": 1772, "cached_tokens": 256}}, "human_critique": null, "stages": [{"id": "extraction", "state": "complete", "reader": "MiniMax-M3.1-Flash", "provider": "minimax"}], "gaps": ["review.independent_reader", "review.machine_critique", "review.human_critique"]}, "execution": {"state": "absent", "status": "not_run", "mode": null, "runs": [], "gaps": ["execution.not_run"]}, "provenance": {"entities": [{"id": "pmid:40842541", "type": "paper", "label": "pmid:40842541"}, {"id": "source:pmid:40842541", "type": "manuscript", "label": "PMID 40842541 full text"}, {"id": "data:geo:GSE135065", "type": "dataset", "label": "GEO GSE135065"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:40842541", "type": "paper", "label": "pmid:40842541"}, {"id": "source:pmid:40842541", "type": "manuscript", "label": "PMID 40842541 full text"}, {"id": "data:geo:GSE135065", "type": "dataset", "label": "GEO GSE135065"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-40842541", "type": "extraction", "used": ["source:pmid:40842541"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE135065", "type": "accession_resolution", "used": ["data:geo:GSE135065"], "agent": "agent:corpus-assay-engine"}], "agents": [{"id": "agent:corpus-assay-engine", "type": "software", "label": "Corpus assay engine"}, {"id": "agent:minimax-reader", "type": "software", "label": "MiniMax literature reader"}], "source_run_id": "reading-sci-2026-11-01", "dsl_catalogue_fingerprint": "343fb30756ba6ec3", "capability_table_fingerprint": "2cb3e42f047d2ecf", "snapshot_id": "ba34b49490d9bfaf"}, "lineage": {"parent_id": "pmid:40842541", "parent_hash": "sha256:8058efcbe34a43de299d61730e700177f055df09e3ccc2a8c9c3809b9d6d5338", "fork_of": null, "generation": 3}, "gaps": ["asset:data:geo:GSE135065 licence not read", "asset:data:geo:GSE135065 not downloaded", "asset:data:geo:GSE15197 licence not read", "asset:data:geo:GSE15197 not downloaded", "asset:data:geo:GSE185691 licence not read", "asset:data:geo:GSE185691 not downloaded", "asset:data:geo:GSE195770 licence not read", "asset:data:geo:GSE195770 not downloaded", "asset:data:geo:GSE53845 licence not read", "asset:data:geo:GSE53845 not downloaded", "execution.not_run", "method.reader_differs_from_extractor", "review.human_critique", "review.independent_reader", "review.machine_critique"], "assays": [{"role": "analysis", "definition_id": "differential_expression", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["Differential gene expression was performed using the limma package (19), which calculates gene expression differences between different groups (IPF group vs. normal control group) through linear modeling."], "order_basis": "analysis_only", "id": "assay:1"}]},"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":"2026-11-04T03:15:57+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}}}