{"id":"W2891413025","doi":"10.1002/mnfr.201800384","title":"Nutrimetabolomics: An Integrative Action for Metabolomic Analyses in Human Nutritional Studies","year":2018,"lang":"en","type":"review","venue":"Molecular Nutrition & Food Research","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":238,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Ministero dell’Istruzione, dell’Università e della Ricerca; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; Agence Nationale de la Recherche; Agència de Gestió d'Ajuts Universitaris i de Recerca; Ministerio de Economía y Competitividad; Generalitat de Catalunya; Joint Programming Initiative A healthy diet for a healthy life; European Regional Development Fund; Centro de Investigación Biomédica en Red Fragilidad y Envejecimiento Saludable","keywords":"Metabolomics; Workflow; Data science; Action (physics); Computer science; Biology; Bioinformatics","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"],"consensus_categories":[],"category_scores_codex":[0.001985091,0.0007982263,0.002306457,0.001946551,0.0005387876,0.0001448343,0.0007908035,0.0007857569,0.00001760775],"category_scores_gemma":[0.0008164273,0.0006949492,0.001090165,0.001373668,0.0006628842,0.00002735698,0.0004220688,0.0008387163,0.00001286698],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002710671,"about_ca_system_score_gemma":0.000357803,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002165686,"about_ca_topic_score_gemma":0.0002088426,"domain_scores_codex":[0.9938177,0.001723536,0.00114538,0.001683547,0.0006440478,0.0009857651],"domain_scores_gemma":[0.9969007,0.0002057448,0.0003904507,0.000918449,0.001390865,0.0001937521],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001856251,0.0105638,0.000007746199,0.0535471,0.01953887,0.00008516222,0.0003860667,0.000008108172,0.7094852,0.03124653,0.03857569,0.1346994],"study_design_scores_gemma":[0.001833793,0.003116231,0.000002348984,0.002414942,0.0006045894,0.00002523311,0.0007926277,0.00000434549,0.06576621,0.006418826,0.9182622,0.0007586296],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.005422466,0.9893937,0.0005838225,0.00005774936,0.0002792601,0.003404162,0.0007119437,0.0000275592,0.0001193498],"genre_scores_gemma":[0.002003874,0.985077,0.003104335,0.00003603682,0.001253767,0.00422587,0.003958343,0.0001458964,0.0001949345],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.8796865,"threshold_uncertainty_score":0.9995502,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4248052048409547,"score_gpt":0.5739197153242279,"score_spread":0.1491145104832732,"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."}}