{"id":"W4382344552","doi":"10.1111/mcn.13523","title":"Multiple micronutrient supplementation cost–benefit tool for informing maternal nutrition policy and investment decisions","year":2023,"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":"Kingston Health Sciences Centre; Nutrition International","funders":"Global Affairs Canada","keywords":"Medicine; Micronutrient; Cost effectiveness; Environmental health; Cost–benefit analysis; Investment (military); Developing country; Intervention (counseling); Environmental resource management; Economic growth; Risk analysis (engineering); Economics; Nursing","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":[0.007611839,0.001541492,0.001451983,0.006807174,0.000397667,0.00364045,0.001817753,0.002101835,0.04681865],"category_scores_gemma":[0.05098416,0.0007207278,0.002487481,0.005095025,0.000254839,0.002738176,0.001989997,0.001966074,0.005211471],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002425511,"about_ca_system_score_gemma":0.00384017,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008860607,"about_ca_topic_score_gemma":0.008478406,"domain_scores_codex":[0.9961739,0.002266799,0.0003794956,0.0002135106,0.0007925389,0.0001737411],"domain_scores_gemma":[0.9627739,0.03101044,0.001801081,0.0007127132,0.00316717,0.0005348312],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001873046,0.0008632144,0.03390131,0.007279821,0.001862785,0.0007303693,0.0005222296,0.2682531,0.0007937188,0.05220116,0.3154564,0.3162628],"study_design_scores_gemma":[0.001879556,0.0008547566,0.02130187,0.006429643,0.001729459,0.0006090614,0.0007478941,0.5509104,0.002219117,0.1052219,0.3075407,0.0005556123],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.09039902,0.007967735,0.2553315,0.02139438,0.001114812,0.006352254,0.3794672,0.02290683,0.2150664],"genre_scores_gemma":[0.4787649,0.00445403,0.4035966,0.003298684,0.0003676109,0.007047686,0.08747021,0.002219987,0.01278043],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04681865,"threshold_uncertainty_score":0.156624,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01710193076908188,"score_gpt":0.2836015704486487,"score_spread":0.2664996396795668,"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."}}