{"id":"W4394180035","doi":"10.6084/m9.figshare.22599464","title":"Additional file 4 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":"Open MIND","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; Metabolomics; Metabolic syndrome; Computational biology; Computer science; Bioinformatics; Medicine; Biology; Internal medicine; Genetics; Obesity","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001286211,0.001540667,0.001353955,0.001898486,0.0007492543,0.001598504,0.002346231,0.0017165,0.3426941],"category_scores_gemma":[0.01201601,0.000538,0.001289635,0.003069878,0.0003556969,0.001034378,0.001142015,0.001266357,0.05175383],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001364433,"about_ca_system_score_gemma":0.002232466,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02258741,"about_ca_topic_score_gemma":0.04099396,"domain_scores_codex":[0.9993559,0.00009678016,0.000105329,0.0002098031,0.0001066206,0.000125657],"domain_scores_gemma":[0.9959782,0.002132324,0.0004191515,0.0004803611,0.0007187995,0.0002711136],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0002537265,0.00008845454,0.008232856,0.001093765,0.00009452763,0.00006284341,0.00002984021,0.0005275335,0.00007821048,0.0003933313,0.9857848,0.003360069],"study_design_scores_gemma":[0.007752873,0.0003492987,0.1051844,0.002755222,0.0006530671,0.0006426449,0.0005475325,0.005501297,0.001361226,0.008961448,0.8660564,0.0002345982],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0002003194,0.00001365329,0.00004402094,0.00004133342,0.000007986569,0.00001695486,0.9994859,0.0000441259,0.0001458067],"genre_scores_gemma":[0.002706217,0.00004081304,0.0003785275,0.0001197985,0.00002244365,0.0004156953,0.9949499,0.00004579814,0.001320831],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.3426941,"threshold_uncertainty_score":0.9375671,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1521080853197874,"score_gpt":0.4444829218603849,"score_spread":0.2923748365405975,"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."}}