{"id":"W3083609993","doi":"","title":"INPUT OF DEEP PHENOTYPING IN THE METABOLIC SYNDROME STRATIFICATION","year":2020,"lang":"fr","type":"preprint","venue":"Prodinra (INRA Bordeaux-Aquitaine)","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; McGill University; Institut Universitaire de Gériatrie de Montréal","funders":"","keywords":"Context (archaeology); Feature selection; Personalized medicine; Computer science; Metabolic syndrome; Workflow; Medicine; Artificial intelligence; Machine learning; Computational biology; Bioinformatics; Internal medicine; Biology; Obesity","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002120292,0.001258675,0.0009805555,0.001122207,0.0003190074,0.001522878,0.000844949,0.0007791455,0.004932709],"category_scores_gemma":[0.005518892,0.0003142618,0.0008672019,0.0008374159,0.0003229312,0.00050163,0.001926668,0.0006836046,0.001796659],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004194253,"about_ca_system_score_gemma":0.001156672,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002916888,"about_ca_topic_score_gemma":0.003503499,"domain_scores_codex":[0.9988781,0.0004970332,0.00005511169,0.0002927882,0.0001559583,0.0001210446],"domain_scores_gemma":[0.9986342,0.0006728971,0.0001150855,0.0002008736,0.0002539905,0.0001229074],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00232623,0.0004044823,0.1390591,0.0009654235,0.001093353,0.0008466456,0.0002808394,0.06569734,0.05172496,0.004308779,0.02491854,0.7083742],"study_design_scores_gemma":[0.0002451136,0.0008344959,0.1088819,0.0004281805,0.0006796077,0.001000944,0.0002232989,0.791387,0.0293431,0.03239318,0.03435723,0.0002260768],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2629652,0.004840031,0.7039358,0.003153472,0.0008134252,0.0004530413,0.01218359,0.006140862,0.0055145],"genre_scores_gemma":[0.7462028,0.001576366,0.2343874,0.0009273004,0.0004920398,0.000502684,0.0115498,0.0004865713,0.003875155],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004932709,"threshold_uncertainty_score":0.01650155,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0207188838372047,"score_gpt":0.265854323914083,"score_spread":0.2451354400768783,"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."}}