{"id":"W4312141125","doi":"10.3390/foods11244099","title":"Food Toxicology and Food Safety: Report of the 3rd International Electronic Conference on Foods: Food, Microbiome, and Health—A Celebration of the 10th Anniversary of Foods’ Impact on Our Wellbeing","year":2022,"lang":"en","type":"article","venue":"Foods","topic":"GABA and Rice Research","field":"Agricultural and Biological Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Bundesanstalt für Landwirtschaft und Ernährung; Institut National de la Recherche Agronomique; Ministério da Ciência, Tecnologia e Ensino Superior; Joint Programming Initiative A healthy diet for a healthy life; Fonds Wetenschappelijk Onderzoek; Ministero delle Politiche Agricole Alimentari e Forestali; Réseau de cancérologie Rossy","keywords":"Food safety; Environmental health; Medicine; Agriculture; Microbiome; Biotechnology; Food science; 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":[],"consensus_categories":[],"category_scores_codex":[0.0005773662,0.0001281764,0.000238376,0.000042511,0.0002831738,0.00001419346,0.0003787655,0.00006471248,0.0000496701],"category_scores_gemma":[0.00004231878,0.00004686414,0.0001248039,0.0003618853,0.0001043139,0.00005125202,0.0003156457,0.0002749015,1.678626e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001003337,"about_ca_system_score_gemma":0.0001342263,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001219681,"about_ca_topic_score_gemma":0.0005941895,"domain_scores_codex":[0.9985278,0.0002663078,0.0003402838,0.0002672456,0.0003204815,0.0002779125],"domain_scores_gemma":[0.9992406,0.0001127669,0.0003904955,0.0001208929,0.00008302621,0.00005217345],"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":[0.0009959739,0.00101847,0.04402452,0.0001483994,0.0005667075,0.000002578197,0.00131322,0.0001765212,0.9071826,0.0203625,0.001085756,0.02312281],"study_design_scores_gemma":[0.0008698307,0.04154164,0.9149271,0.00009903629,0.00002593912,0.0001316154,0.002796444,0.0001779678,0.03387414,0.002988333,0.002353305,0.0002146637],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9883775,0.0002628815,0.000002521526,0.01012692,0.0001496988,0.0004111107,0.0002178835,0.000007213176,0.0004443386],"genre_scores_gemma":[0.999611,0.0001105863,0.00001118248,0.000113981,0.00004180037,0.000009672495,0.00002373143,0.00000188103,0.0000761192],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8733084,"threshold_uncertainty_score":0.2177972,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03203372027755926,"score_gpt":0.2895018214218243,"score_spread":0.2574681011442651,"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."}}