{"id":"W4294550849","doi":"10.1016/j.numecd.2022.08.018","title":"Potential reductions in ultra-processed food consumption substantially improve population cardiometabolic-related dietary nutrient profiles in eight countries","year":2022,"lang":"en","type":"article","venue":"Nutrition Metabolism and Cardiovascular Diseases","topic":"Consumer Attitudes and Food Labeling","field":"Medicine","cited_by":34,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal","funders":"Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Nutrient; Energy density; Saturated fat; Nutrient density; Dietary fiber; Population; Total energy; Environmental health; Population density; Cohort; Medicine; Food science; Geography; Biology; Cholesterol; Internal medicine; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004520295,0.0002794983,0.0008912039,0.0007142421,0.0003278936,0.00007805233,0.00009064042,0.0001018682,0.0001073125],"category_scores_gemma":[0.00005105953,0.0002982114,0.000569215,0.0008385564,0.0001093935,0.0003616377,0.0000580963,0.0003421865,0.000002526372],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009451662,"about_ca_system_score_gemma":0.0001123972,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002777053,"about_ca_topic_score_gemma":0.0000273333,"domain_scores_codex":[0.9972672,0.0003066456,0.0006051366,0.0006496795,0.0007979867,0.0003732811],"domain_scores_gemma":[0.9991586,0.00002700319,0.0001029922,0.0003935175,0.0001438232,0.0001740791],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.01255507,0.0166351,0.7318845,0.01546566,0.02750426,0.00168663,0.004288355,0.0135086,0.06445745,0.01383078,0.0004930386,0.09769052],"study_design_scores_gemma":[0.0157468,0.0002234733,0.9646655,0.0002552457,0.006821364,0.0002973273,0.001610258,0.0003290691,0.00153543,0.001159203,0.006668211,0.0006881467],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7152501,0.2823403,0.00006079927,0.0001681453,0.0004337222,0.00112379,0.0005395093,0.00006673134,0.00001692814],"genre_scores_gemma":[0.9790279,0.01860894,0.0001103817,0.00005391154,0.000213026,0.0007495565,0.001190448,0.00003658077,0.000009213447],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2637779,"threshold_uncertainty_score":0.999947,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00949742131576149,"score_gpt":0.230073307038039,"score_spread":0.2205758857222775,"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."}}