{"id":"W2799275727","doi":"10.1016/j.ehb.2018.09.002","title":"Decomposition of changes in the consumption of macronutrients in Vietnam between 2004 and 2014","year":2018,"lang":"en","type":"article","venue":"Economics & Human Biology","topic":"Economics of Agriculture and Food Markets","field":"Economics, Econometrics and Finance","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Centre de Coopération Internationale en Recherche Agronomique pour le Développement; Institut National de la Recherche Agronomique; Agence Nationale de la Recherche; Trường Đại học Duy Tân; Wilfrid Laurier University","keywords":"Calorie; Nutrition transition; Per capita; Population; Consumption (sociology); Vietnamese; Demography; Distribution (mathematics); Economics; Demographic economics; Biology; Medicine; Environmental health; Mathematics; Overweight; Body mass index; Endocrinology","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":[0.0007358736,0.0001122964,0.0004386838,0.0002696024,0.00003953173,0.00001156425,0.0002165468,0.0001313796,0.00008446736],"category_scores_gemma":[0.00001131515,0.0001081697,0.0000412396,0.000052766,0.0002392498,0.00008801616,0.00005950378,0.00009691928,0.00003700147],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004910017,"about_ca_system_score_gemma":0.000005379944,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001766252,"about_ca_topic_score_gemma":0.002349403,"domain_scores_codex":[0.9988436,0.00003486398,0.00064878,0.0002824455,0.000005446117,0.000184878],"domain_scores_gemma":[0.9992114,0.00008473729,0.0004709169,0.000194453,0.00001489713,0.00002355405],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002575819,0.00006106256,0.9110963,0.00002328649,0.00002613183,1.732786e-7,0.0005045229,0.000001645501,0.0002604737,0.0873889,0.0001751781,0.0004365755],"study_design_scores_gemma":[0.0009333086,0.0003923599,0.9243111,0.00001752067,0.000005451993,0.000002500565,0.00006484866,0.00002338767,0.0007318837,0.07028795,0.003071486,0.0001581702],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9946062,0.00106766,0.00001602775,0.000367431,0.0001118129,0.0002111819,0.00014643,0.00000331306,0.003469938],"genre_scores_gemma":[0.9986012,0.0009811356,0.0000935225,0.000111285,0.0001081721,0.00001372123,0.00006928416,0.000006868754,0.00001479406],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01710094,"threshold_uncertainty_score":0.4411032,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02935550726840041,"score_gpt":0.2652631584236605,"score_spread":0.2359076511552601,"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."}}