{"id":"W2887614096","doi":"10.1002/sim.7693","title":"Comparing predictive abilities of longitudinal child growth models","year":2018,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Child Nutrition and Water Access","field":"Nursing","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Medical Research Council; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Fogarty International Center; Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul; Department for International Development; Nutricia Research Foundation; British Heart Foundation; Wellcome Trust; Department for International Development, UK Government; World Health Organization; Ministério da Saúde; South African Medical Research Council; Department of Science and Technology, Ministry of Science and Technology, India; Wenner-Gren Foundation; Carolina Population Center, University of North Carolina at Chapel Hill; Andrew W. Mellon Foundation; Bill and Melinda Gates Foundation; National Institutes of Health; Indian Council of Medical Research; International Development Research Centre; United States Agency for International Development; National Science Foundation","keywords":"Estimation; Raw data; Computer science; Psychological intervention; Work (physics); Longitudinal data; Child development; Econometrics; Data science; Data mining; Psychology; Economics; Developmental psychology","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":[0.0002505031,0.0001174691,0.0003313379,0.0001862489,0.00007116619,0.000007200632,0.0001475604,0.00004026968,0.0001124264],"category_scores_gemma":[0.0002703863,0.0001010442,0.00001571948,0.0001895722,0.0007357259,0.0001012293,0.00003964142,0.0001772354,0.000003402608],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005802474,"about_ca_system_score_gemma":0.000007288448,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006398083,"about_ca_topic_score_gemma":0.0002583222,"domain_scores_codex":[0.9988509,0.00005634941,0.0004049298,0.0002035569,0.0002884848,0.0001957219],"domain_scores_gemma":[0.9992406,0.0002133207,0.00008805312,0.0001617693,0.0002373787,0.00005890544],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002424671,0.0006111269,0.7698198,0.001192014,0.00009266187,0.0000312357,0.02789277,0.0003031729,0.0001458796,0.1572348,0.03911613,0.001135783],"study_design_scores_gemma":[0.005387743,0.001862231,0.4782795,0.00171209,0.0001012055,0.00002763395,0.001150729,0.05892701,0.003662445,0.4483581,0.000238218,0.000293023],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6244585,0.0005504622,0.3042397,0.00422585,0.003069733,0.0008212759,0.0004750407,0.000135696,0.06202377],"genre_scores_gemma":[0.9955204,0.00002350799,0.003633393,0.0001875164,0.0005457628,0.000005863202,0.00005107464,0.00001407901,0.00001844253],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3710619,"threshold_uncertainty_score":0.4120464,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03696819290147681,"score_gpt":0.3174421561511075,"score_spread":0.2804739632496307,"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."}}