{"id":"W4394389674","doi":"10.6084/m9.figshare.22599458","title":"Additional file 2 of Machine learning of plasma metabolome identifies biomarker panels for metabolic syndrome: findings from the China Suboptimal Health Cohort","year":2023,"lang":"en","type":"dataset","venue":"Figshare","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Centre for Addiction and Mental Health; University of Alberta; University of Ottawa","funders":"","keywords":"Metabolome; Biomarker; Cohort; Computational biology; Metabolomics; Computer science; Medicine; Bioinformatics; Biology; Internal medicine; Genetics","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":["metaresearch","metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0006852554,0.0005271095,0.001592933,0.0003402616,0.0012656,0.0000246973,0.001141146,0.0007788839,0.9688717],"category_scores_gemma":[0.03042677,0.0004226162,0.0004555859,0.0007269581,0.00008786048,0.0001898741,0.0006475755,0.001900653,0.01169614],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001275218,"about_ca_system_score_gemma":0.001627718,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01202314,"about_ca_topic_score_gemma":0.003313982,"domain_scores_codex":[0.9943214,0.001053861,0.00208027,0.0007578452,0.0008525322,0.000934056],"domain_scores_gemma":[0.9675806,0.02827464,0.002438812,0.0008545252,0.0006387501,0.0002126407],"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":[0.00006508075,0.00003911533,0.00006310987,0.002546754,0.0003391768,0.000007705103,0.0002757202,0.00001230767,0.000001407035,0.000003037455,0.9964437,0.0002028469],"study_design_scores_gemma":[0.00009824168,0.00009559021,0.02390506,0.01105183,0.00009382334,0.000004098763,0.0004999549,0.0005791789,0.00001354671,0.00007170258,0.9632853,0.000301674],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00009804334,0.0007700322,3.972635e-7,0.0002972329,0.000564749,0.002958109,0.9951947,0.0001047367,0.00001196936],"genre_scores_gemma":[0.00004492373,0.0001617108,0.0001170643,0.0002085153,0.0003220864,0.01008388,0.9875219,0.0001102255,0.001429662],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.9571755,"threshold_uncertainty_score":0.9998226,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1460833786457021,"score_gpt":0.4136449999710946,"score_spread":0.2675616213253925,"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."}}