{"id":"W4387579778","doi":"10.1038/s43016-023-00851-5","title":"Global food nutrients analysis reveals alarming gaps and daunting challenges","year":2023,"lang":"en","type":"article","venue":"Nature Food","topic":"Child Nutrition and Water Access","field":"Nursing","cited_by":92,"is_retracted":false,"has_abstract":false,"ca_institutions":"Ministry of Agriculture","funders":"","keywords":"Food security; Per capita; Micronutrient; Nutrient; Environmental health; Agriculture; Population; Business; Agricultural economics; Geography; Development economics; Natural resource economics; Biology; Medicine; Economics; Ecology","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":[0.0002643602,0.0001902116,0.0003161041,0.0003513289,0.0002088247,0.0001190299,0.0001986908,0.0003266355,0.000009532183],"category_scores_gemma":[0.0001366294,0.0001828068,0.0001584857,0.001774951,0.00003019984,0.0001922412,0.0001227119,0.0003455121,0.00001316076],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005030371,"about_ca_system_score_gemma":0.000003998564,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006087335,"about_ca_topic_score_gemma":0.0001138472,"domain_scores_codex":[0.9985166,0.00007893902,0.0002133418,0.0004747839,0.0003335431,0.0003828042],"domain_scores_gemma":[0.9993572,0.00008625517,0.00009568354,0.0002648116,0.00008047371,0.0001155497],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0007550136,0.0007457163,0.907883,0.001795329,0.004229621,0.00008517981,0.005437619,0.00008570453,0.0007985322,0.009412739,0.03149045,0.03728113],"study_design_scores_gemma":[0.003399584,0.0006717308,0.9288825,0.0003465984,0.001094735,0.00003485489,0.0007932998,0.0003587005,0.005433775,0.02016021,0.03804382,0.0007801278],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.978127,0.01316388,0.000008882402,0.006359235,0.0006457224,0.0002106014,0.0001380753,0.000314766,0.001031854],"genre_scores_gemma":[0.9984137,0.0003756852,0.0001474755,0.0005493014,0.0003697165,0.000009967481,0.00009306412,0.00001969885,0.00002140379],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.036501,"threshold_uncertainty_score":0.7454646,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01867981605855326,"score_gpt":0.293638178325882,"score_spread":0.2749583622673287,"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."}}