{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.002501115,0.0004699607,0.001778158,0.0002912362,0.001375625,0.00005221052,0.001784813,0.0006187751,0.7042252],"category_scores_gemma":[0.007174089,0.0003676219,0.0003072647,0.0006488158,0.0003225885,0.0002748631,0.001074912,0.001574545,0.004951864],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001152796,"about_ca_system_score_gemma":0.001990134,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05024125,"about_ca_topic_score_gemma":0.01166828,"domain_scores_codex":[0.9938895,0.001377737,0.002319111,0.0008410284,0.0007276238,0.0008449827],"domain_scores_gemma":[0.9765363,0.01957414,0.002395649,0.0008942485,0.0003974472,0.0002021635],"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":[0.0001703802,0.00006470733,0.0003192094,0.0003620553,0.0004369149,0.000007180054,0.0006866087,0.00001050823,0.000007511113,0.000005198387,0.9960638,0.001865928],"study_design_scores_gemma":[0.0001546862,0.0001332841,0.01748672,0.001929393,0.0001936363,0.000004648796,0.00166253,0.0004401666,0.00004345742,0.0001047166,0.9775611,0.0002856703],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00469129,0.0003200515,0.000004651904,0.0005792489,0.000954792,0.004334288,0.9890649,0.000008645609,0.00004216263],"genre_scores_gemma":[0.0001572164,0.0003570891,0.001393299,0.00009811624,0.0001863394,0.003046702,0.989962,0.0000787338,0.004720441],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.6992733,"threshold_uncertainty_score":0.9999244,"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."}}