{"id":"W4283016154","doi":"10.3390/metabo13030373","title":"Global and Partial Effect Assessment in Metabolic Syndrome Explored by Metabolomics","year":2023,"lang":"en","type":"article","venue":"Metabolites","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fonds de Recherche du Québec - Santé; Réseau québécois de recherche sur le vieillissement; Fonds de recherche du Québec; Agence Nationale de la Recherche; Université de Sherbrooke","keywords":"Metabolomics; Computer science; Path (computing); Path coefficient; Block (permutation group theory); Regression; Variance (accounting); Data mining; Path analysis (statistics); Statistics; Machine learning; Mathematics; Bioinformatics; Biology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008922752,0.0003705531,0.0007420933,0.0001696354,0.0001119417,0.00007299092,0.0002374892,0.0001568691,0.00002275792],"category_scores_gemma":[0.0002185146,0.000323776,0.0001484896,0.0008261093,0.0001288997,0.00001490396,0.0003670655,0.0001336478,0.00001946906],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001498446,"about_ca_system_score_gemma":0.00004870385,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006646516,"about_ca_topic_score_gemma":0.00002852469,"domain_scores_codex":[0.9977381,0.0002528175,0.0004051079,0.0007050254,0.0002457915,0.0006531063],"domain_scores_gemma":[0.9992434,0.00005055761,0.0001044965,0.0004092254,0.00004051706,0.0001517501],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0001778145,0.0002025534,0.3563108,0.00007354093,0.0008832608,0.00005195265,0.00008182921,0.00005742729,0.6107373,0.01496771,0.006745295,0.009710462],"study_design_scores_gemma":[0.00313416,0.0004280282,0.5989959,0.00001154879,0.0003013221,0.00005661291,0.0001836182,0.0003518239,0.1131439,0.001065383,0.2814817,0.0008459244],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9655464,0.03253344,0.0002134768,0.0002387815,0.00038034,0.0003575203,0.0002049985,0.00005906194,0.00046599],"genre_scores_gemma":[0.985777,0.01238379,0.0007437894,0.000146755,0.0001266336,0.0002120068,0.0002849348,0.00003453153,0.0002905464],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4975934,"threshold_uncertainty_score":0.9999214,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008678275587211487,"score_gpt":0.2743752844541317,"score_spread":0.2656970088669202,"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."}}