{"id":"W2756000048","doi":"10.1186/s12978-017-0361-y","title":"Female genital mutilation/cutting: sharing data and experiences to accelerate eradication and improve care","year":2017,"lang":"en","type":"article","venue":"Reproductive Health","topic":"Female Genital Mutilation/Cutting Issues","field":"Medicine","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Francophone University Association","funders":"Université de Lausanne; Hôpitaux Universitaires de Genève; World Health Organization","keywords":"Female circumcision; Reproductive medicine; Subject (documents); Public relations; Medicine; Health care; Public health; Nursing; Medical education; Political science; Gynecology; Law; Computer science; Library science","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":[0.0005833464,0.0001729142,0.000298781,0.0001017589,0.0009389383,0.0002023296,0.0002592632,0.00005422392,0.00001931655],"category_scores_gemma":[0.001188685,0.0001655449,0.00001620855,0.00008948346,0.000178445,0.000616546,0.0005774263,0.000124823,0.0000223145],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001024766,"about_ca_system_score_gemma":0.0001651462,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003176199,"about_ca_topic_score_gemma":0.0000212822,"domain_scores_codex":[0.9977934,0.0000338476,0.0003231835,0.001316432,0.0002343738,0.0002988096],"domain_scores_gemma":[0.9972124,0.00002813196,0.0002939129,0.001971305,0.0002207791,0.0002734502],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003342544,0.0001176857,0.5040268,0.001085859,0.00009279014,0.00001486934,0.3288988,0.000006018937,0.008424055,0.0002720521,0.0004702253,0.1562565],"study_design_scores_gemma":[0.000952002,0.0007673475,0.9000756,0.0002770758,0.00005837373,0.0001325815,0.07923949,0.001432654,0.01267899,0.00008326441,0.003891261,0.0004114027],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9888588,0.002434094,0.0001478528,0.005802174,0.0002805931,0.001074834,0.00002623361,0.00006358348,0.001311829],"genre_scores_gemma":[0.9902128,0.00006467601,0.007989047,0.0002339058,0.0006939125,0.00005600197,0.00008334014,0.0000270727,0.0006392601],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3960488,"threshold_uncertainty_score":0.7221649,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1133291106122988,"score_gpt":0.4159055341595357,"score_spread":0.3025764235472369,"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."}}