{"id":"W1977785348","doi":"10.2217/pme.12.79","title":"Applying Genomics to Nutrition and Lifestyle Modification","year":2012,"lang":"en","type":"article","venue":"Personalized Medicine","topic":"Nutrition, Genetics, and Disease","field":"Biochemistry, Genetics and Molecular Biology","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Advanced Foods and Materials Network","keywords":"Genomics; Medicine; Nutrigenomics; Computational biology; Gerontology; Genetics; Bioinformatics; Biology; Genome; Gene","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.0001694945,0.00009920327,0.0001057523,0.00004718228,0.0000728466,0.000006521001,0.0000519593,0.00006303659,0.00003850203],"category_scores_gemma":[0.00009437116,0.00009527273,0.00002589711,0.0000533236,0.00008087383,0.000003803581,0.00002657264,0.00003179496,0.000009982527],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001571232,"about_ca_system_score_gemma":0.00001875598,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001234327,"about_ca_topic_score_gemma":0.000002016308,"domain_scores_codex":[0.9993576,0.00002882395,0.000125595,0.0001910289,0.00010603,0.0001908641],"domain_scores_gemma":[0.9994472,0.00000905748,0.00003417929,0.0001531749,0.00006005782,0.0002963048],"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":[0.0002127888,0.0001839116,0.00754236,0.0001141719,0.0000241085,5.52007e-7,0.0006727062,0.000001988449,0.973716,0.0004181124,0.01222328,0.004890083],"study_design_scores_gemma":[0.003973087,0.0002718047,0.01169267,0.00007913246,0.00009921775,0.00003354152,0.001505855,0.00006338662,0.03615488,0.0002714758,0.9455491,0.0003058772],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9747033,0.01793112,0.004382345,0.001482774,0.0001850897,0.000611212,0.000022127,0.00001685975,0.0006651542],"genre_scores_gemma":[0.991174,0.002094649,0.002060044,0.001794839,0.001892714,0.0002825606,0.0003014574,0.00001752771,0.0003821561],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.937561,"threshold_uncertainty_score":0.3885109,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02474159473619256,"score_gpt":0.2997347542928981,"score_spread":0.2749931595567055,"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."}}