{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001222935,0.001407137,0.001388019,0.001931808,0.0007893284,0.001569941,0.002100026,0.001458088,0.493861],"category_scores_gemma":[0.01385739,0.0005111675,0.001301027,0.00342712,0.0003301464,0.001134991,0.0010619,0.001186611,0.06035654],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001058637,"about_ca_system_score_gemma":0.002098308,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01533614,"about_ca_topic_score_gemma":0.02386182,"domain_scores_codex":[0.9994222,0.00008262459,0.00009569915,0.0002032628,0.00009119396,0.0001050833],"domain_scores_gemma":[0.9945126,0.003305373,0.0005151716,0.0005730287,0.0008045228,0.0002894305],"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":[0.0003030595,0.00007966434,0.006687519,0.001312576,0.00009812113,0.00006807155,0.00003210018,0.0005341114,0.00008506395,0.0004685519,0.9862258,0.004105439],"study_design_scores_gemma":[0.009384199,0.0004424571,0.09661304,0.003117272,0.0007597512,0.0008619205,0.000551977,0.00538539,0.001310691,0.01349798,0.8678118,0.0002635557],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001716464,0.00001357142,0.00005584365,0.00003556188,0.000007639027,0.00002167162,0.9994944,0.00004910292,0.0001504778],"genre_scores_gemma":[0.003926093,0.00006265006,0.0006295632,0.0001785272,0.00003494648,0.0006627146,0.9926166,0.00009349862,0.001795423],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.493861,"threshold_uncertainty_score":0.7219459,"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."}}