{"data":{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37293297", "kind": "science-record", "revision": 4, "content_hash": "sha256:2063cba0cdea5c6cdd522d297e8e3f8c4644881241e21775ba5750b2226f2407", "created_at": "2026-11-01T19:02:01Z", "updated_at": "2026-11-01T22:52:05Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of AKI signatures and classification patterns in ccRCC based on machine learning.", "doi": "10.3389/fmed.2023.1195678", "pmid": "37293297", "authors": ["Li Wang", "Fei Peng", "Zhen Hua Li", "Yu Fei Deng", "Meng Na Ruan", "Zhi Guo Mao", "Lin Li"], "journal": "Frontiers in medicine", "year": 2023, "abstract": "Background: Acute kidney injury can be mitigated if detected early. There are limited biomarkers for predicting acute kidney injury (AKI). In this study, we used public databases with machine learning algorithms to identify novel biomarkers to predict AKI. In addition, the interaction between AKI and clear cell renal cell carcinoma (ccRCC) remain elusive.\nMethods: Four public AKI datasets (GSE126805, GSE139061, GSE30718, and GSE90861) treated as discovery datasets and one (GSE43974) treated as a validation dataset were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between AKI and normal kidney tissues were identified using the R package limma. Four machine learning algorithms were used to identify the novel AKI biomarkers. The correlations between the seven biomarkers and immune cells or their components were calculated using the R package ggcor. Furthermore, two distinct ccRCC subtypes with different prognoses and immune characteristics were identified and verified using seven novel biomarkers.\nResults: Seven robust AKI signatures were identified using the four machine learning methods. The immune infiltration analysis revealed that the numbers of activated CD4 T cells, CD56dim natural killer cells, eosinophils, mast cells, memory B cells, natural killer T cells, neutrophils, T follicular helper cells, and type 1 T helper cells were significantly higher in the AKI cluster. The nomogram for prediction of AKI risk demonstrated satisfactory discrimination with an Area Under the Curve (AUC) of 0.919 in the training set and 0.945 in the testing set. In addition, the calibration plot demonstrated few errors between the predicted and actual values. In a separate analysis, the immune components and cellular differences between the two ccRCC subtypes based on their AKI signatures were compared. Patients in the CS1 had better overall survival, progression-free survival, drug sensitivity, and survival probability.\nConclusion: Our study identified seven distinct AKI-related biomarkers based on four machine learning methods and proposed a nomogram for stratified AKI risk prediction. We also confirmed that AKI signatures were valuable for predicting ccRCC prognosis. The current work not only sheds light on the early prediction of AKI, but also provides new insights into the correlation between AKI and ccRCC.", "claims": [{"id": "claim-method", "statement": "We adopted four machine-learning algorithms, including Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression, Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and support vector machine (SVM), to select reliable biomarkers to distinguish AKI from normal tissues.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37293297", "locator": "sections[6] \"Identification and verification of AKI-related biomarkers\" paragraph 2", "quote": "We adopted four machine-learning algorithms, including Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression, Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and support vector machine (SVM), to select reliable biomarkers to distinguish AKI from normal tissues."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary material.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37293297", "locator": "sections[23] \"Data availability statement\" paragraph 2", "quote": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary material."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The nomogram for prediction of AKI risk demonstrated satisfactory discrimination with an Area Under the Curve (AUC) of 0.919 in the training set and 0.945 in the testing set.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37293297", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "The nomogram for prediction of AKI risk demonstrated satisfactory discrimination with an Area Under the Curve (AUC) of 0.919 in the training set and 0.945 in the testing set."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "The nomogram for prediction of AKI risk demonstrated satisfactory discrimination with an Area Under the Curve (AUC) of 0.919 in the training set and 0.945 in the testing set.", "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:37293297"], "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:E-MTAB-3267"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "blocked", "blocked_on": "method:machine_learning_random_forest", "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:E-MTAB-3267"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": ["step:run_analysis blocked on method:machine_learning_random_forest"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:37293297", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fmed.2023.1195678", "sha256": "sha256:1e25b08ab4d1773b6951e8968f3ad34a50e963f78f1827b6ad2ac6f9a4caf4ac", "bytes": 38725, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fmed.2023.1195678", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC10244623", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:E-MTAB-3267", "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=E-MTAB-3267", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "E-MTAB-3267", "version": null, "note": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary material.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary material.", "access_statement_kind": "data_availability_statement", "sample_accessions": [], "sample_count": null, "group_sizes": null, "group_sizes_basis": null, "platform": null, "taxon": null, "title": null, "resolution_checked_at": "2026-10-31T18:28:58Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}, {"id": "data:geo:GSE126805", "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=GSE126805", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE126805", "version": null, "note": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary material.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary material.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM3613602", "GSM3613603", "GSM3613604", "GSM3613605", "GSM3613606", "GSM3613607", "GSM3613608", "GSM3613609", "GSM3613610", "GSM3613611", "GSM3613612", "GSM3613613", "GSM3613614", "GSM3613615", "GSM3613616", "GSM3613617", "GSM3613618", "GSM3613619", "GSM3613620", "GSM3613621", "GSM3613622", "GSM3613623", "GSM3613624", "GSM3613625", "GSM3613626", "GSM3613627", "GSM3613628", "GSM3613629", "GSM3613630", "GSM3613631", "GSM3613632", "GSM3613633", "GSM3613634", "GSM3613635", "GSM3613636", "GSM3613637", "GSM3613638", "GSM3613639", "GSM3613640", "GSM3613641", "GSM3613642", "GSM3613643", "GSM3613644", "GSM3613645", "GSM3613646", "GSM3613647", "GSM3613648", "GSM3613649", "GSM3613650", "GSM3613651", "GSM3613652", "GSM3613653", "GSM3613654", "GSM3613655", "GSM3613656", "GSM3613657", "GSM3613658", "GSM3613659", "GSM3613660", "GSM3613661", "GSM3613662", "GSM3613663", "GSM3613664", "GSM3613665", "GSM3613666", "GSM3613667", "GSM3613668", "GSM3613669", "GSM3613670", "GSM3613671", "GSM3613672", "GSM3613673", "GSM3613674", "GSM3613675", "GSM3613676", "GSM3613677", "GSM3613678", "GSM3613679", "GSM3613680", "GSM3613681", "GSM3613682", "GSM3613683", "GSM3613684", "GSM3613685", "GSM3613686", "GSM3613687", "GSM3613688", "GSM3613689", "GSM3613690", "GSM3613691", "GSM3613692", "GSM3613693", "GSM3613694", "GSM3613695", "GSM3613696", "GSM3613697", "GSM3613698", "GSM3613699", "GSM3613700", "GSM3613701", "GSM3613702", "GSM3613703", "GSM3613704", "GSM3613705", "GSM3613706", "GSM3613707", "GSM3613708", "GSM3613709", "GSM3613710", "GSM3613711", "GSM3613712", "GSM3613713", "GSM3613714", "GSM3613715", "GSM3613716", "GSM3613717", "GSM3613718", "GSM3613719", "GSM3613720", "GSM3613721", "GSM3613722", "GSM3613723", "GSM3613724", "GSM3613725", "GSM3613726", "GSM3613727", "GSM3613728", "GSM3613729", "GSM3613730", "GSM3613731", "GSM3613732", "GSM3613733", "GSM3613734", "GSM3613735", "GSM3613736", "GSM3613737", "GSM3613738", "GSM3613739", "GSM3613740", "GSM3613741", "GSM3613742", "GSM3613743", "GSM3613744", "GSM3613745", "GSM3613746", "GSM3613747", "GSM3613748", "GSM3613749", "GSM3613750", "GSM3613751", "GSM3613752", "GSM3613753", "GSM3613754", "GSM3613755", "GSM3613756", "GSM3613757", "GSM3613758", "GSM3613759", "GSM3613760", "GSM3613761", "GSM3613762", "GSM3613763", "GSM3613764"], "sample_count": 163, "group_sizes": null, "group_sizes_basis": null, "platform": "21290", "taxon": "Homo sapiens", "title": "Transcriptional trajectories of human kidney injury progression", "resolution_checked_at": "2026-11-01T18:46:34Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}, {"id": "data:geo:GSE139061", "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=GSE139061", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE139061", "version": null, "note": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary material.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary material.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM4128538", "GSM4128539", "GSM4128540", "GSM4128541", "GSM4128542", "GSM4128543", "GSM4128544", "GSM4128545", "GSM4128546", "GSM4128547", "GSM4128548", "GSM4128549", "GSM4128550", "GSM4128551", "GSM4128552", "GSM4128553", "GSM4128554", "GSM4128555", "GSM4128556", "GSM4128557", "GSM4128558", "GSM4128559", "GSM4128560", "GSM4128561", "GSM4128562", "GSM4128563", "GSM4128564", "GSM4128565", "GSM4128566", "GSM4128567", "GSM4128568", "GSM4128569", "GSM4128570", "GSM4128571", "GSM4128572", "GSM4128573", "GSM4128574", "GSM4128575", "GSM4128576", "GSM4128577", "GSM4128578", "GSM4128579", "GSM4128580", "GSM4128581", "GSM4128582", "GSM4128583", "GSM4128584", "GSM4128585"], "sample_count": 48, "group_sizes": null, "group_sizes_basis": null, "platform": "20301", "taxon": "Homo sapiens", "title": "Transcriptomic Signatures of Kidney Injury in Human Renal Biopsy Specimens", "resolution_checked_at": "2026-11-01T18:46:34Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}, {"id": "data:geo:GSE22541", "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=GSE22541", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE22541", "version": null, "note": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary material.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary material.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM359477", "GSM359478", "GSM359479", "GSM359480", "GSM359481", "GSM359482", "GSM359483", "GSM359484", "GSM359485", "GSM359486", "GSM359487", "GSM359488", "GSM359489", "GSM359490", "GSM359491", "GSM359492", "GSM359493", "GSM359494", "GSM359495", "GSM359496", "GSM559577", "GSM559578", "GSM559579", "GSM559580", "GSM559581", "GSM559582", "GSM559583", "GSM559584", "GSM559585", "GSM559586", "GSM559587", "GSM559588", "GSM559589", "GSM559590", "GSM559591", "GSM559592", "GSM559593", "GSM559594", "GSM559595", "GSM559596", "GSM559597", "GSM559598", "GSM559599", "GSM559600", "GSM559705", "GSM559706", "GSM559707", "GSM559708", "GSM559709", "GSM559710", "GSM559711", "GSM559712", "GSM559713", "GSM559714", "GSM559715", "GSM559716", "GSM559717", "GSM559718", "GSM559719", "GSM559720", "GSM559721", "GSM559722", "GSM559723", "GSM559724", "GSM559725", "GSM559726", "GSM559727", "GSM559728"], "sample_count": 68, "group_sizes": null, "group_sizes_basis": null, "platform": "570", "taxon": "Homo sapiens", "title": "Expression data from pulmonary metastases and primary tumors of clear-cell renal cell carcinoma (ccRCC) with different disease-free survivals", "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:GSE30718", "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=GSE30718", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE30718", "version": null, "note": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary material.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary material.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM761707", "GSM761708", "GSM761709", "GSM761710", "GSM761711", "GSM761712", "GSM761713", "GSM761714", "GSM761715", "GSM761716", "GSM761717", "GSM761718", "GSM761719", "GSM761720", "GSM761721", "GSM761722", "GSM761723", "GSM761724", "GSM761725", "GSM761726", "GSM761727", "GSM761728", "GSM761729", "GSM761730", "GSM761731", "GSM761732", "GSM761733", "GSM761734", "GSM761735", "GSM761736", "GSM761737", "GSM761738", "GSM761739", "GSM761740", "GSM761741", "GSM761742", "GSM761743", "GSM761744", "GSM761745", "GSM761746", "GSM761747", "GSM761748", "GSM761749", "GSM761750", "GSM761751", "GSM761752", "GSM761753"], "sample_count": 47, "group_sizes": null, "group_sizes_basis": null, "platform": "570", "taxon": "Homo sapiens", "title": "Molecular Phenotypes of Acute Kidney Injury in Kidney Transplants", "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:GSE43974", "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=GSE43974", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE43974", "version": null, "note": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary material.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary material.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM1075188", "GSM1075189", "GSM1075190", "GSM1075191", "GSM1075192", "GSM1075193", "GSM1075194", "GSM1075195", "GSM1075196", "GSM1075197", "GSM1075198", "GSM1075199", "GSM1075200", "GSM1075201", "GSM1075202", "GSM1075203", "GSM1075204", "GSM1075205", "GSM1075206", "GSM1075207", "GSM1075208", "GSM1075209", "GSM1075210", "GSM1075211", "GSM1075212", "GSM1075213", "GSM1075214", "GSM1075215", "GSM1075216", "GSM1075217", "GSM1075218", "GSM1075219", "GSM1075220", "GSM1075221", "GSM1075222", "GSM1075223", "GSM1075224", "GSM1075225", "GSM1075226", "GSM1075227", "GSM1075228", "GSM1075229", "GSM1075230", "GSM1075231", "GSM1075232", "GSM1075233", "GSM1075234", "GSM1075235", "GSM1075236", "GSM1075237", "GSM1075238", "GSM1075239", "GSM1075240", "GSM1075241", "GSM1075242", "GSM1075243", "GSM1075244", "GSM1075245", "GSM1075246", "GSM1075247", "GSM1075248", "GSM1075249", "GSM1075250", "GSM1075251", "GSM1075252", "GSM1075253", "GSM1075254", "GSM1075255", "GSM1075256", "GSM1075257", "GSM1075258", "GSM1075259", "GSM1075260", "GSM1075261", "GSM1075262", "GSM1075263", "GSM1075264", "GSM1075265", "GSM1075266", "GSM1075267", "GSM1075268", "GSM1075269", "GSM1075270", "GSM1075271", "GSM1075272", "GSM1075273", "GSM1075274", "GSM1075275", "GSM1075276", "GSM1075277", "GSM1075278", "GSM1075279", "GSM1075280", "GSM1075281", "GSM1075282", "GSM1075283", "GSM1075284", "GSM1075285", "GSM1075286", "GSM1075287", "GSM1075288", "GSM1075289", "GSM1075290", "GSM1075291", "GSM1075292", "GSM1075293", "GSM1075294", "GSM1075295", "GSM1075296", "GSM1075297", "GSM1075298", "GSM1075299", "GSM1075300", "GSM1075301", "GSM1075302", "GSM1075303", "GSM1075304", "GSM1075305", "GSM1075306", "GSM1075307", "GSM1075308", "GSM1075309", "GSM1075310", "GSM1075311", "GSM1075312", "GSM1075313", "GSM1075314", "GSM1075315", "GSM1075316", "GSM1075317", "GSM1075318", "GSM1075319", "GSM1075320", "GSM1075321", "GSM1075322", "GSM1075323", "GSM1075324", "GSM1075325", "GSM1075326", "GSM1075327", "GSM1075328", "GSM1075329", "GSM1075330", "GSM1075331", "GSM1075332", "GSM1075333", "GSM1075334", "GSM1075335", "GSM1075336", "GSM1075337", "GSM1075338", "GSM1075339", "GSM1075340", "GSM1075341", "GSM1075342", "GSM1075343", "GSM1075344", "GSM1075345", "GSM1075346", "GSM1075347", "GSM1075348", "GSM1075349", "GSM1075350", "GSM1075351", "GSM1075352", "GSM1075353", "GSM1075354", "GSM1075355", "GSM1075356", "GSM1075357", "GSM1075358", "GSM1075359", "GSM1075360", "GSM1075361", "GSM1075362", "GSM1075363", "GSM1075364", "GSM1075365", "GSM1075366", "GSM1075367", "GSM1075368", "GSM1075369", "GSM1075370", "GSM1075371", "GSM1075372", "GSM1075373", "GSM1075374", "GSM1075375", "GSM1075376", "GSM1075377", "GSM1075378", "GSM1075379", "GSM1075380", "GSM1075381", "GSM1075382", "GSM1075383", "GSM1075384", "GSM1075385", "GSM1075386", "GSM1075387", "GSM1075388", "GSM1075389", "GSM1075390", "GSM1075391", "GSM1075392", "GSM1075393", "GSM1075394", "GSM1075395", "GSM1075396", "GSM1075397", "GSM1075398", "GSM1075399", "GSM1075400", "GSM1075401", "GSM1075402", "GSM1075403", "GSM1075404", "GSM1075405", "GSM1075406", "GSM1075407", "GSM1075408", "GSM1075409", "GSM1075410", "GSM1075411", "GSM1075412", "GSM1075413", "GSM1075414", "GSM1075415", "GSM1075416", "GSM1075417", "GSM1075418", "GSM1075419", "GSM1075420", "GSM1075421", "GSM1075422", "GSM1075423", "GSM1075424", "GSM1075425", "GSM1075426", "GSM1075427", "GSM1075428", "GSM1075429", "GSM1075430", "GSM1075431", "GSM1075432", "GSM1075433", "GSM1075434", "GSM1075435", "GSM1075436", "GSM1075437", "GSM1075438", "GSM1075439", "GSM1075440", "GSM1075441", "GSM1075442", "GSM1075443", "GSM1075444", "GSM1075445", "GSM1075446", "GSM1075447", "GSM1075448", "GSM1075449", "GSM1075450", "GSM1075451", "GSM1075452", "GSM1075453", "GSM1075454", "GSM1075455", "GSM1075456", "GSM1075457", "GSM1075458", "GSM1075459", "GSM1075460", "GSM1075461", "GSM1075462", "GSM1075463", "GSM1075464", "GSM1075465", "GSM1075466", "GSM1075467", "GSM1075468", "GSM1075469", "GSM1075470", "GSM1075471", "GSM1075472", "GSM1075473", "GSM1075474", "GSM1075475", "GSM1075476", "GSM1075477", "GSM1075478", "GSM1075479", "GSM1075480", "GSM1075481", "GSM1075482", "GSM1075483", "GSM1075484", "GSM1075485", "GSM1075486", "GSM1075487", "GSM1075488", "GSM1075489", "GSM1075490", "GSM1075491", "GSM1075492", "GSM1075493", "GSM1075494", "GSM1075495", "GSM1075496", "GSM1075497", "GSM1075498", "GSM1075499", "GSM1075500", "GSM1075501", "GSM1075502", "GSM1075503", "GSM1075504", "GSM1075505", "GSM1075506", "GSM1075507", "GSM1075508", "GSM1075509", "GSM1075510", "GSM1075511", "GSM1075512", "GSM1075513", "GSM1075514", "GSM1075515", "GSM1075516", "GSM1075517", "GSM1075518", "GSM1075519", "GSM1075520", "GSM1075521", "GSM1075522", "GSM1075523", "GSM1075524", "GSM1075525", "GSM1075526", "GSM1075527", "GSM1075528", "GSM1075529", "GSM1075530", "GSM1075531", "GSM1075532", "GSM1075533", "GSM1075534", "GSM1075535", "GSM1075536", "GSM1075537", "GSM1075538", "GSM1075539", "GSM1075540", "GSM1075541", "GSM1075542", "GSM1075543", "GSM1075544", "GSM1075545", "GSM1075546", "GSM1075547", "GSM1075548", "GSM1075549", "GSM1075550", "GSM1075551", "GSM1075552", "GSM1075553", "GSM1075554", "GSM1075555", "GSM1075556", "GSM1075557", "GSM1075558", "GSM1075559", "GSM1075560", "GSM1075561", "GSM1075562", "GSM1075563", "GSM1075564", "GSM1075565", "GSM1075566", "GSM1075567", "GSM1075568", "GSM1075569", "GSM1075570", "GSM1075571", "GSM1075572", "GSM1075573", "GSM1075574", "GSM1075575", "GSM1075576", "GSM1075577", "GSM1075578", "GSM1075579", "GSM1075580", "GSM1075581", "GSM1075582", "GSM1075583", "GSM1075584", "GSM1075585", "GSM1075586", "GSM1075587", "GSM1075588", "GSM1075589", "GSM1075590", "GSM1075591", "GSM1075592", "GSM1075593", "GSM1075594", "GSM1075595", "GSM1075596", "GSM1075597", "GSM1075598", "GSM1075599", "GSM1075600", "GSM1075601", "GSM1075602", "GSM1075603", "GSM1075604", "GSM1075605", "GSM1075606", "GSM1075607", "GSM1075608", "GSM1075609", "GSM1075610", "GSM1075611", "GSM1075612", "GSM1075613", "GSM1075614", "GSM1075615", "GSM1075616", "GSM1075617", "GSM1075618", "GSM1075619", "GSM1075620", "GSM1075621", "GSM1075622", "GSM1075623", "GSM1075624", "GSM1075625", "GSM1075626", "GSM1075627", "GSM1075628", "GSM1075629", "GSM1075630", "GSM1075631", "GSM1075632", "GSM1075633", "GSM1075634", "GSM1075635", "GSM1075636", "GSM1075637", "GSM1075638", "GSM1075639", "GSM1075640", "GSM1075641", "GSM1075642", "GSM1075643", "GSM1075644", "GSM1075645", "GSM1075646", "GSM1075647", "GSM1075648", "GSM1075649", "GSM1075650", "GSM1075651", "GSM1075652", "GSM1075653", "GSM1075654", "GSM1075655", "GSM1075656", "GSM1075657", "GSM1075658", "GSM1075659", "GSM1075660", "GSM1075661", "GSM1075662", "GSM1075663", "GSM1075664", "GSM1075665", "GSM1075666", "GSM1075667", "GSM1075668", "GSM1075669", "GSM1075670", "GSM1075671", "GSM1075672", "GSM1075673", "GSM1075674", "GSM1075675", "GSM1075676", "GSM1075677", "GSM1075678", "GSM1075679", "GSM1075680", "GSM1075681", "GSM1075682", "GSM1075683", "GSM1075684", "GSM1075685", "GSM1075686", "GSM1075687", "GSM1075688", "GSM1075689", "GSM1075690", "GSM1075691", "GSM1075692", "GSM1075693", "GSM1075694", "GSM1075695", "GSM1075696", "GSM1075697", "GSM1075698", "GSM1075699", "GSM1075700", "GSM1075701", "GSM1075702", "GSM1075703", "GSM1075704", "GSM1075705", "GSM1075706", "GSM1075707", "GSM1075708", "GSM1075709", "GSM1075710", "GSM1075711", "GSM1075712", "GSM1075713", "GSM1075714", "GSM1075715", "GSM1075716", "GSM1075717", "GSM1075718", "GSM1075719", "GSM1075720", "GSM1075721", "GSM1075722", "GSM1075723", "GSM1075724", "GSM1075725", "GSM1075726", "GSM1075727", "GSM1075728", "GSM1075729", "GSM1075730", "GSM1075731", "GSM1075732", "GSM1075733", "GSM1075734", "GSM1075735", "GSM1075736", "GSM1075737", "GSM1075738", "GSM1075739", "GSM1075740", "GSM1075741"], "sample_count": 554, "group_sizes": null, "group_sizes_basis": null, "platform": "10558", "taxon": "Homo sapiens", "title": "Pathways for intervention to optimize donor organ quality uncovered: a genome wide gene expression study", "resolution_checked_at": "2026-11-01T18:46:34Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}, {"id": "data:geo:GSE90861", "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=GSE90861", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE90861", "version": null, "note": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary material.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary material.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM2416565", "GSM2416566", "GSM2416567", "GSM2416568", "GSM2416569", "GSM2416570", "GSM2416571", "GSM2416572", "GSM2416573", "GSM2416574", "GSM2416575", "GSM2416576", "GSM2416577", "GSM2416578", "GSM2416579", "GSM2416580", "GSM2416581", "GSM2416582", "GSM2416583", "GSM2416584", "GSM2416585", "GSM2416586", "GSM2416587", "GSM2416588", "GSM2416589", "GSM2416590", "GSM2416591", "GSM2416592", "GSM2416593", "GSM2416594", "GSM2416595", "GSM2416596", "GSM2416597", "GSM2416598", "GSM2416599", "GSM2416600", "GSM2416601", "GSM2416602", "GSM2416603", "GSM2416604", "GSM2416605", "GSM2416606", "GSM2416607", "GSM2416608", "GSM2416609", "GSM2416610"], "sample_count": 46, "group_sizes": null, "group_sizes_basis": null, "platform": "18573", "taxon": "Homo sapiens", "title": "A Molecular signature for Delayed Graft Function [RNA-Seq]", "resolution_checked_at": "2026-11-01T18:46:34Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}], "gaps": ["asset:data:geo:E-MTAB-3267 not downloaded", "asset:data:geo:E-MTAB-3267 licence not read", "asset:data:geo:GSE126805 not downloaded", "asset:data:geo:GSE126805 licence not read", "asset:data:geo:GSE139061 not downloaded", "asset:data:geo:GSE139061 licence not read", "asset:data:geo:GSE22541 not downloaded", "asset:data:geo:GSE22541 licence not read", "asset:data:geo:GSE30718 not downloaded", "asset:data:geo:GSE30718 licence not read", "asset:data:geo:GSE43974 not downloaded", "asset:data:geo:GSE43974 licence not read", "asset:data:geo:GSE90861 not downloaded", "asset:data:geo:GSE90861 licence not read"]}, "analysis": {"state": "partial", "methods": [{"id": "method-1", "family": "machine_learning", "specific": "random_forest", "label": "Machine learning", "dsl_operations": [], "missing_operations": ["machine_learning_random_forest"], "expressible": false, "parameters": {}, "data_shapes": ["labelled_feature_matrix"], "evidence": {"score": 1.0, "band": "verbatim", "quote": "We adopted four machine-learning algorithms, including Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression, Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and support vector machine (SVM), to select reliable biomarkers to distinguish AKI from normal tissues."}}, {"id": "method-2", "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": 0.7, "band": "medium", "quote": "limma"}}, {"id": "method-3", "family": "machine_learning", "specific": "support_vector_machine", "label": "Machine learning", "dsl_operations": ["train_classifier", "cross_validate", "roc_auc"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["labelled_feature_matrix"], "evidence": {"score": 0.8, "band": "high", "quote": "support vector machine"}}, {"id": "method-4", "family": "machine_learning", "specific": "gradient_boosting", "label": "Machine learning", "dsl_operations": ["train_classifier", "cross_validate", "roc_auc"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["labelled_feature_matrix"], "evidence": {"score": 0.8, "band": "high", "quote": "Gradient Boosting"}}, {"id": "method-5", "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": 0.8, "band": "high", "quote": "logistic regression"}}, {"id": "method-6", "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": [], "gaps": ["method.reader_differs_from_extractor", "method.machine_learning_random_forest"]}, "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": false, "required_operations": [], "missing_operations": ["machine_learning_random_forest"], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: random_forest."}, "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": ["GSE126805", "GSE139061", "GSE30718", "GSE90861", "GSE43974", "GSE22541", "E-MTAB-3267"], "resolved_accessions": ["GSE126805", "GSE139061", "GSE30718", "GSE90861", "GSE43974", "GSE22541", "E-MTAB-3267"], "statements": ["The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary material."], "basis": "The paper names GSE126805, GSE139061, GSE30718, GSE90861, GSE43974, GSE22541, E-MTAB-3267 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": [], "assets": ["source:pmid:37293297", "data:geo:E-MTAB-3267"], "steps": []}, "must_build": {"dsl_operations": [{"operation": "machine_learning_random_forest", "unlocks_papers": null, "priority": null}], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:E-MTAB-3267"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T18:43:27Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 10805, "completion_tokens": 3516, "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:37293297", "type": "paper", "label": "pmid:37293297"}, {"id": "source:pmid:37293297", "type": "manuscript", "label": "PMID 37293297 full text"}, {"id": "data:geo:E-MTAB-3267", "type": "dataset", "label": "GEO E-MTAB-3267"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:37293297", "type": "paper", "label": "pmid:37293297"}, {"id": "source:pmid:37293297", "type": "manuscript", "label": "PMID 37293297 full text"}, {"id": "data:geo:E-MTAB-3267", "type": "dataset", "label": "GEO E-MTAB-3267"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-37293297", "type": "extraction", "used": ["source:pmid:37293297"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-E-MTAB-3267", "type": "accession_resolution", "used": ["data:geo:E-MTAB-3267"], "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:37293297", "parent_hash": "sha256:b1090a89c888dccda4d054e53d03628b50fd74081301a4bc6be35349f1c430d6", "fork_of": null, "generation": 3}, "gaps": ["asset:data:geo:E-MTAB-3267 licence not read", "asset:data:geo:E-MTAB-3267 not downloaded", "asset:data:geo:GSE126805 licence not read", "asset:data:geo:GSE126805 not downloaded", "asset:data:geo:GSE139061 licence not read", "asset:data:geo:GSE139061 not downloaded", "asset:data:geo:GSE22541 licence not read", "asset:data:geo:GSE22541 not downloaded", "asset:data:geo:GSE30718 licence not read", "asset:data:geo:GSE30718 not downloaded", "asset:data:geo:GSE43974 licence not read", "asset:data:geo:GSE43974 not downloaded", "asset:data:geo:GSE90861 licence not read", "asset:data:geo:GSE90861 not downloaded", "execution.not_run", "method.machine_learning_random_forest", "method.reader_differs_from_extractor", "review.human_critique", "review.independent_reader", "review.machine_critique", "step:run_analysis blocked on method:machine_learning_random_forest"], "assays": [{"role": "data_generation", "definition_id": "methylation_array", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.85, "evidence": ["SORT-ABS, quanTIseq, MCP-counter, xCell, and EPIC algorithms. MHC, major histocompatibility co"], "order_basis": "paper_text_order:5371", "id": "assay:1"}, {"role": "analysis", "definition_id": "machine_learning", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["We adopted four machine-learning algorithms, including Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression, Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and support vector machine (SVM), to select reliable biomarkers to distinguish AKI from normal tissues."], "order_basis": "consumes_earlier_output", "id": "assay:2"}]},"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":"2026-11-04T03:01:25+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}}}