{"id":"W4301104290","doi":"10.1007/978-3-031-41215-8_5","title":"Sex Selection for Nonmedical Reasons","year":2023,"lang":"en","type":"article","venue":"","topic":"Neuroethics, Human Enhancement, Biomedical Innovations","field":"Neuroscience","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; Mount Sinai Hospital","funders":"","keywords":"Sex selection; Selection (genetic algorithm); Autonomy; Position (finance); Psychology; Ethical issues; Subject (documents); Social psychology; Political science; Business; Engineering ethics; Computer science; Sociology; Engineering; Law; Demography","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":[0.0003537415,0.0000864394,0.00009392443,0.0001670928,0.0002811843,0.00003491538,0.0001847951,0.00007841951,0.0005851808],"category_scores_gemma":[0.003043489,0.00007851714,0.00004122593,0.001179313,0.0002225322,0.00009075912,0.00006388451,0.0002061696,0.0006417704],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003145461,"about_ca_system_score_gemma":0.00006007991,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005590744,"about_ca_topic_score_gemma":0.00001063398,"domain_scores_codex":[0.9986708,0.00004660177,0.0002088394,0.0003782654,0.0003660638,0.0003294323],"domain_scores_gemma":[0.9988791,0.0007827031,0.00004615405,0.0001540922,0.00004226659,0.00009565506],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000009254401,0.00008710466,0.00008912529,0.00002119173,0.000003469869,0.00000380632,0.0001101855,0.000004402623,0.6652483,0.1058185,0.2238711,0.004733477],"study_design_scores_gemma":[0.0005784218,0.000206592,0.0003555955,0.00001249707,0.000008242049,0.00001267757,0.00006272638,0.013967,0.7431531,0.02211084,0.2193208,0.0002115484],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4486342,0.000003977394,0.3446806,0.1070729,0.004793134,0.002901492,0.0001470307,0.006213688,0.08555292],"genre_scores_gemma":[0.9251982,0.00002142782,0.001075038,0.009384094,0.0004114429,0.0002401543,0.00001991619,0.00003297021,0.06361676],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.476564,"threshold_uncertainty_score":0.8248875,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1327557500031351,"score_gpt":0.3826000634437339,"score_spread":0.2498443134405988,"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."}}