{"id":"W3085517443","doi":"10.1038/s41598-020-71612-8","title":"Brazilian Maternal and Child Nutrition Consortium: establishment, data harmonization and basic characteristics","year":2020,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Gestational Diabetes Research and Management","field":"Medicine","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Ministério da Saúde; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Bill and Melinda Gates Foundation","keywords":"Pregnancy; Harmonization; Outlier; Medicine; Consistency (knowledge bases); Longitudinal data; Multilevel model; Environmental health; Demography; Statistics; Computer science; Biology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.2298858,0.00127078,0.003929449,0.02021097,0.003230004,0.005531515,0.004691547,0.002161885,0.005366263],"category_scores_gemma":[0.3160051,0.002102857,0.005706773,0.03197375,0.00189443,0.00223752,0.01026072,0.00212022,0.001166995],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006924911,"about_ca_system_score_gemma":0.06944247,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04233307,"about_ca_topic_score_gemma":0.04628391,"domain_scores_codex":[0.7987831,0.1053072,0.06290896,0.007366752,0.02240166,0.003232454],"domain_scores_gemma":[0.716471,0.06602994,0.0420593,0.09022439,0.07951381,0.005701672],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.004480402,0.0006194914,0.3065993,0.05111858,0.01325012,0.0007248718,0.01454087,0.003885086,0.003066967,0.04670692,0.1733768,0.3816306],"study_design_scores_gemma":[0.002283128,0.0005094284,0.3012961,0.02700628,0.005487966,0.0005273109,0.003055023,0.002124133,0.00278339,0.01330284,0.6412566,0.0003678192],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.09327277,0.02425544,0.2220921,0.01658626,0.002065256,0.1842148,0.4195146,0.001719958,0.0362789],"genre_scores_gemma":[0.1094177,0.005212412,0.3284172,0.002355394,0.0003896834,0.3914673,0.1599772,0.0008193037,0.001943814],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.2298858,"threshold_uncertainty_score":0.9496879,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03035070894793313,"score_gpt":0.2805729368107395,"score_spread":0.2502222278628064,"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."}}