{"id":"W4407888374","doi":"10.1007/s12561-025-09475-8","title":"Introduction to Special Issue on Machine Learning in Biomedical Sciences","year":2025,"lang":"en","type":"article","venue":"Statistics in Biosciences","topic":"Genetics, Bioinformatics, and Biomedical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Biostatistics; Computer science; Data science; Artificial intelligence; Medicine; Public health; Pathology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001179241,0.0001151875,0.000134461,0.0005297228,0.0001430768,0.00007009975,0.0004085024,0.00009899736,0.0001152387],"category_scores_gemma":[0.002414472,0.00009423434,0.00001793984,0.001039553,0.0008578451,0.0000050914,0.000183807,0.0001897026,0.00004783793],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003895446,"about_ca_system_score_gemma":0.0002542673,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008574386,"about_ca_topic_score_gemma":0.0007268173,"domain_scores_codex":[0.9982961,0.00007886952,0.0003268952,0.0004176015,0.0004805764,0.0003998945],"domain_scores_gemma":[0.9995987,0.00006659302,0.00004074588,0.0001373914,0.0000504971,0.0001061074],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004151642,0.0008921261,0.04277747,0.0002001274,0.00002204638,0.0000292259,0.001044667,0.001210227,0.08541878,0.01517896,0.2902145,0.5625967],"study_design_scores_gemma":[0.0006528845,0.002427427,0.01931367,0.00007681718,0.000003453636,0.000002652531,0.001026114,0.006111136,0.02015275,0.00235367,0.9475644,0.0003150206],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.796179,0.0006477359,0.04630998,0.06708642,0.01393701,0.002097119,0.0005978646,0.00007341101,0.07307145],"genre_scores_gemma":[0.9288396,0.001366789,0.0492833,0.002591752,0.007995483,0.000044223,0.0002535166,0.00001616666,0.009609128],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6573499,"threshold_uncertainty_score":0.3842765,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01336694504180941,"score_gpt":0.3339881433114388,"score_spread":0.3206211982696294,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}