{"id":"W4412604297","doi":"10.1136/bmjnph-2025-001238","title":"Advancing nutritional genomics in the era of big data, multiomics and artificial intelligence","year":2025,"lang":"en","type":"article","venue":"BMJ Nutrition Prevention & Health","topic":"Nutrition, Genetics, and Disease","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Big data; Genomics; Data science; Biology; Computer science; Genome; Genetics","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":[],"consensus_categories":[],"category_scores_codex":[0.00104213,0.0001073036,0.0001541527,0.0001031759,0.0001218435,0.00002437128,0.0002156735,0.00008694644,0.000005848531],"category_scores_gemma":[0.0001478275,0.0001115929,0.00005489839,0.000184757,0.00009491258,0.00001140299,0.00009350872,0.0001111423,9.497567e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003510847,"about_ca_system_score_gemma":0.0003667218,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004422861,"about_ca_topic_score_gemma":0.0003848804,"domain_scores_codex":[0.9984756,0.0002917209,0.0005724599,0.0003427533,0.0001288954,0.0001885475],"domain_scores_gemma":[0.9992079,0.00004973228,0.000166171,0.000412909,0.0001031859,0.0000601394],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.005970474,0.01978799,0.0168395,0.009955386,0.0002559315,0.00001510737,0.001280944,0.0006427108,0.1365339,0.05609945,0.04590774,0.7067109],"study_design_scores_gemma":[0.01224687,0.00291596,0.09195518,0.004765507,0.0002403342,0.0001523693,0.01618264,0.007974345,0.09540062,0.6314547,0.1349085,0.001803029],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7997773,0.01717205,0.1672538,0.01159329,0.0005413226,0.002746196,0.0007766486,0.00001743333,0.000121955],"genre_scores_gemma":[0.9806484,0.008600838,0.007338696,0.001044097,0.0003785854,0.0001183656,0.001840703,0.00000921787,0.00002108067],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7049078,"threshold_uncertainty_score":0.4550627,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04518881287834198,"score_gpt":0.3671812056269802,"score_spread":0.3219923927486382,"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."}}