{"id":"W2921611290","doi":"10.1093/ije/dyy233","title":"Infant nutrition and growth: trends and inequalities in four population-based birth cohorts in Pelotas, Brazil, 1982–2015","year":2018,"lang":"en","type":"article","venue":"International Journal of Epidemiology","topic":"Child Nutrition and Water Access","field":"Nursing","cited_by":45,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Associação Brasileira de Saúde Coletiva; European Commission; International Development Research Centre; Wellcome Trust; World Health Organization","keywords":"Overweight; Wasting; Anthropometry; Malnutrition; Medicine; Demography; Socioeconomic status; Obesity; Population; Cohort; Birth weight; Nutrition transition; Double burden; Pediatrics; Environmental health; Pregnancy; Endocrinology; Biology; Internal medicine","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009215772,0.0003186229,0.0003098327,0.001395932,0.0004117802,0.0006148498,0.0004318632,0.0002621271,0.0009911337],"category_scores_gemma":[0.002042413,0.0003438773,0.0006945964,0.002072103,0.0002664581,0.0004341239,0.0009278706,0.0003588148,0.0001805286],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001032216,"about_ca_system_score_gemma":0.0008757835,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.191233,"about_ca_topic_score_gemma":0.2244409,"domain_scores_codex":[0.9996561,0.00004993336,0.00005544108,0.00008415314,0.00007327217,0.00008118313],"domain_scores_gemma":[0.9991918,0.00008225343,0.000339554,0.00006889515,0.0001987458,0.0001185874],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002415067,0.00001191273,0.9973822,0.00003318561,0.00005014631,0.00003675236,0.0004295043,0.00002829901,0.0001886438,0.00004776837,0.000168547,0.001598948],"study_design_scores_gemma":[9.76283e-7,0.000008218723,0.9993103,0.00001491138,0.00001278953,0.00003505146,0.0003097016,0.00004326045,0.00002708905,0.00001035973,0.0002255092,0.000001847862],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9927964,0.001063176,0.0001676185,0.0001892061,0.00001030978,0.00003555846,0.004739343,0.00001358186,0.0009849505],"genre_scores_gemma":[0.9969579,0.0006313207,0.0001607397,0.00003057003,0.00000403323,0.00003341467,0.001973025,0.000004029685,0.0002049604],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.191233,"threshold_uncertainty_score":0.3802399,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04458997194847757,"score_gpt":0.3685463450123725,"score_spread":0.3239563730638949,"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."}}