{"id":"W3092252791","doi":"10.1093/eurpub/ckaa165.415","title":"Improving Gender Equality & Nutrition Data for Women and Children","year":2020,"lang":"en","type":"article","venue":"European Journal of Public Health","topic":"Child Nutrition and Water Access","field":"Nursing","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Impact","funders":"","keywords":"Empowerment; Gender equality; Malnutrition; Government (linguistics); Inequality; Gender inequality; Women's empowerment; Reproductive health; Environmental health; Political science; Economic growth; Medicine; Economics; Sociology; Gender studies","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.06379008,0.0003801865,0.0005427023,0.005037877,0.002183951,0.004742343,0.001817081,0.001039547,0.0185224],"category_scores_gemma":[0.1554268,0.0003986567,0.0006088809,0.006933771,0.001581895,0.005458539,0.005955615,0.001505516,0.004036868],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003700021,"about_ca_system_score_gemma":0.01524594,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01413499,"about_ca_topic_score_gemma":0.01645178,"domain_scores_codex":[0.9488952,0.03463832,0.004769228,0.002181727,0.008256284,0.00125923],"domain_scores_gemma":[0.8486975,0.0786457,0.01756982,0.02278573,0.02757857,0.004722681],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004167934,0.0004647403,0.2653869,0.00143397,0.0001129022,0.0002537448,0.008019079,0.00102901,0.001063617,0.05756056,0.1009988,0.5632599],"study_design_scores_gemma":[0.0001014411,0.0005224673,0.1800149,0.005134573,0.0000756255,0.0003933258,0.01111508,0.003722143,0.006880063,0.04127692,0.7505782,0.0001851591],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.1667524,0.005155961,0.1414685,0.1326091,0.002360417,0.006136144,0.1919089,0.002848101,0.3507605],"genre_scores_gemma":[0.6932697,0.003892946,0.219766,0.006939965,0.0005726307,0.008000591,0.03998791,0.0005439543,0.02702634],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.06379008,"threshold_uncertainty_score":0.3373582,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2157763385634677,"score_gpt":0.3571510560044156,"score_spread":0.1413747174409479,"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."}}