{"id":"W3081313108","doi":"10.1111/mcn.12770","title":"Reducing malnutrition in Cambodia. A modeling exercise to prioritize multisectoral interventions","year":2020,"lang":"en","type":"article","venue":"Maternal and Child Nutrition","topic":"Child Nutrition and Water Access","field":"Nursing","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Council of Ministers of Education","funders":"UNICEF","keywords":"Wasting; Malnutrition; Sanitation; Environmental health; Medicine; Psychological intervention; Poverty; Hygiene; Child mortality; Socioeconomic status; Inequality; Socioeconomics; Population; Economic growth; Economics","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":[],"consensus_categories":[],"category_scores_codex":[0.0000887042,0.0001966448,0.0002817001,0.0002501684,0.000159541,0.0002016084,0.0001247102,0.00008847618,0.00004546485],"category_scores_gemma":[0.00002105937,0.0002154905,0.0001246208,0.0002782465,0.00002081685,0.0003636811,0.00007104668,0.0002340918,0.00002884576],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006391009,"about_ca_system_score_gemma":0.000002542931,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004008533,"about_ca_topic_score_gemma":0.00005376887,"domain_scores_codex":[0.9985741,0.00005788767,0.0004576173,0.0004330753,0.0001815461,0.0002957096],"domain_scores_gemma":[0.9995421,0.000009189459,0.00005549211,0.0001038684,0.00005087136,0.0002385184],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.05863423,0.01749353,0.49886,0.07650956,0.0002948891,0.001326919,0.03796085,0.01289501,0.131056,0.001840391,0.02331773,0.1398109],"study_design_scores_gemma":[0.06176251,0.0035587,0.3531244,0.09335956,0.0004897679,0.0007621681,0.001685334,0.2153511,0.2475247,0.01187454,0.005876933,0.004630187],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9803252,0.001019932,0.0008372798,0.01639674,0.0002978844,0.0008203184,0.00007433906,0.0001462019,0.0000820845],"genre_scores_gemma":[0.9965887,0.0002520463,0.001185476,0.001225409,0.000535822,0.0001081802,0.00006729929,0.00003020124,0.000006831991],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2024561,"threshold_uncertainty_score":0.8787447,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02948066252153925,"score_gpt":0.278736830010554,"score_spread":0.2492561674890147,"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."}}