{"id":"W2078629634","doi":"10.1016/j.ijgo.2005.05.001","title":"Sex selection: Treating different cases differently","year":2005,"lang":"en","type":"article","venue":"International Journal of Gynecology & Obstetrics","topic":"Demographic Trends and Gender Preferences","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Sex selection; Girl; Selection (genetic algorithm); Redress; Sex ratio; Abortion; Incentive; Convention on the Elimination of All Forms of Discrimination Against Women; Psychology; Preference; Demography; Political science; Developmental psychology; Law; Pregnancy; Economics; Sociology; Biology; Computer science; Population; Human rights","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":[0.0003857115,0.0001318951,0.0002546895,0.000576263,0.0002560707,0.0001272776,0.0005759174,0.0001378992,0.0008561744],"category_scores_gemma":[0.002568247,0.000107764,0.0001884604,0.0003402171,0.0001519474,0.0002991654,0.00005098154,0.0002962966,0.0000134044],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003301878,"about_ca_system_score_gemma":0.0001852752,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008165297,"about_ca_topic_score_gemma":0.002149733,"domain_scores_codex":[0.9980877,0.0002332954,0.0005200353,0.0001504981,0.0007421854,0.0002662625],"domain_scores_gemma":[0.9958558,0.002818054,0.0005008187,0.00006256623,0.0006090021,0.0001537734],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00003295626,0.0004731883,0.4252241,0.000002634601,0.0004053315,0.0001377222,0.005613694,0.0001006756,0.00006757708,0.007278946,0.002425626,0.5582376],"study_design_scores_gemma":[0.003507984,0.00106218,0.5200765,0.00007143707,0.0002465947,0.0005879867,0.01203301,0.0003909172,0.0008025807,0.006262465,0.4542961,0.0006623371],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9782686,0.0008708466,0.000426145,0.004692,0.004143618,0.00006007881,0.000007805866,0.00003362844,0.01149724],"genre_scores_gemma":[0.9947366,0.0008719938,0.0005748572,0.0002720299,0.001250745,0.000002844049,0.000002711917,0.000008028106,0.002280199],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5575752,"threshold_uncertainty_score":0.9374509,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03530576530068521,"score_gpt":0.330256115875678,"score_spread":0.2949503505749929,"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."}}