{"id":"W3154039159","doi":"10.1016/j.sste.2021.100422","title":"Identifying climatic and non-climatic determinants of malnutrition prevalence in Bangladesh: A country-wide cross-sectional spatial analysis","year":2021,"lang":"en","type":"article","venue":"Spatial and Spatio-temporal Epidemiology","topic":"Child Nutrition and Water Access","field":"Nursing","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Malnutrition; Sanitation; Precipitation; Climatology; Environmental science; Geography; Climate change; Spatial variability; Environmental health; Physical geography; Medicine; Meteorology; Ecology; Statistics; Environmental engineering","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":[0.001364936,0.0002735253,0.00107115,0.0004430999,0.0001880784,0.00007020881,0.0001358136,0.0002786466,0.0001946453],"category_scores_gemma":[0.001411637,0.0002710342,0.0001574642,0.0006382442,0.0003265972,0.0003421227,0.0001319406,0.0002776216,0.000004463491],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007332553,"about_ca_system_score_gemma":0.00004065787,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.007960314,"about_ca_topic_score_gemma":0.01974494,"domain_scores_codex":[0.9963516,0.0007268548,0.001569637,0.0006879768,0.0002236851,0.000440275],"domain_scores_gemma":[0.997552,0.0012186,0.0006107333,0.000265114,0.0002144753,0.0001390444],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003907383,0.000160237,0.9966841,0.001362925,0.00006067637,0.00002604337,0.0002463477,0.00008325809,0.0001325639,0.00003020362,0.00002777283,0.0007950954],"study_design_scores_gemma":[0.001738226,0.000160463,0.9442428,0.0002751419,0.0001842688,0.00004454457,0.00003284802,0.04772992,0.0008605731,0.004452762,0.00002793471,0.0002504534],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9916499,0.0006969715,0.005966664,0.0006618933,0.0004289594,0.0003753572,0.0001589837,0.00002692999,0.00003437566],"genre_scores_gemma":[0.9964885,0.0002789102,0.001972271,0.0004450728,0.0001573979,0.00004431481,0.0005695456,0.00001865045,0.00002534752],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05244127,"threshold_uncertainty_score":0.9999742,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04183727239295104,"score_gpt":0.3460294908765862,"score_spread":0.3041922184836351,"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."}}