{"id":"W4394079474","doi":"10.6084/m9.figshare.20677966","title":"Additional file 1 of Opening the black box: interpretable machine learning for predictor finding of metabolic syndrome","year":2022,"lang":"en","type":"dataset","venue":"Open MIND","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Black box; Metabolic syndrome; Computer science; Machine learning; Artificial intelligence; Biology; Endocrinology; Diabetes mellitus","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001445072,0.0002862218,0.0009418389,0.0001962741,0.00122534,0.00003084329,0.001921357,0.0003275555,0.9945717],"category_scores_gemma":[0.006506905,0.0002333897,0.0001838307,0.0003486406,0.000184778,0.0002045839,0.001925844,0.001977238,0.0007388391],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001124546,"about_ca_system_score_gemma":0.001454401,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002848579,"about_ca_topic_score_gemma":0.00156657,"domain_scores_codex":[0.9959609,0.001024098,0.001474199,0.0005056586,0.0004814787,0.0005536731],"domain_scores_gemma":[0.9831361,0.01389241,0.001899063,0.0006810949,0.0002836281,0.000107752],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001167394,0.00004431005,0.00004773737,0.0003959154,0.0001173494,0.000006041871,0.001055515,0.0001225668,0.000002096964,0.00000682659,0.9970204,0.00106453],"study_design_scores_gemma":[0.000103472,0.0002103192,0.00003910288,0.001757675,0.0001058197,0.000007797819,0.003942909,0.001079929,0.00001841026,0.00004431723,0.9925013,0.0001889125],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0002128533,0.0001518362,0.000007588015,0.00009745407,0.0004838199,0.003250899,0.9946972,0.00000235303,0.001096056],"genre_scores_gemma":[0.0001382609,0.00003360717,0.001177691,0.0000709474,0.000151031,0.003598501,0.9841597,0.00004324182,0.01062701],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.9938329,"threshold_uncertainty_score":0.9517356,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1531917193070078,"score_gpt":0.4548649496319371,"score_spread":0.3016732303249292,"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."}}