{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001651103,0.0002485921,0.0002378925,0.0005192477,0.0005555481,0.0003708194,0.000106644,0.0001069551,0.00001933723],"category_scores_gemma":[0.00004548948,0.0002447306,0.00008758937,0.0002667694,0.00006164652,0.0005176745,0.00008959323,0.0001169214,0.00001868299],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001233716,"about_ca_system_score_gemma":0.000005796715,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002142048,"about_ca_topic_score_gemma":0.00004646523,"domain_scores_codex":[0.9984198,0.00002949869,0.0004853796,0.0003795065,0.0002570619,0.0004287221],"domain_scores_gemma":[0.9993677,0.0001149984,0.0001339059,0.0001378382,0.00009030969,0.0001552338],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.02542312,0.005459052,0.5129528,0.01416651,0.000474675,0.0001158842,0.005879088,0.0001167829,0.07348857,0.03426069,0.07440966,0.2532532],"study_design_scores_gemma":[0.02982097,0.00084678,0.3898911,0.003096764,0.0001184115,0.0004415243,0.0003198881,0.002302939,0.342154,0.05364778,0.1764775,0.0008824277],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9886854,0.0002403187,0.0003213214,0.007103068,0.0004132658,0.002136102,0.0008430559,0.0001862548,0.00007123691],"genre_scores_gemma":[0.992265,0.001603667,0.001159992,0.001780122,0.0009753416,0.0008033466,0.001287096,0.00003899698,0.00008641087],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2686654,"threshold_uncertainty_score":0.9979826,"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."}}