{"id":"W3125043127","doi":"","title":"Ethical and Legal Issues in Assisted Reproductive Technology","year":2006,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Reproductive Health and Technologies","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Ethical issues; Reproductive technology; Assisted reproductive technology; Business; Selection (genetic algorithm); Political science; Law; Risk analysis (engineering); Law and economics; Pregnancy; Engineering ethics; Engineering; Economics; Computer science; Biology","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":[0.1108285,0.0006076072,0.001196717,0.001093594,0.008639917,0.01024546,0.002611578,0.02822885,0.004171351],"category_scores_gemma":[0.1378789,0.0006774883,0.0008418939,0.001704953,0.05305817,0.007084702,0.006268446,0.02840682,0.001730124],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003021787,"about_ca_system_score_gemma":0.01289121,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001966319,"about_ca_topic_score_gemma":0.002360861,"domain_scores_codex":[0.8758512,0.09089991,0.006847559,0.004075641,0.01965279,0.00267277],"domain_scores_gemma":[0.7866604,0.1849101,0.005173298,0.008422178,0.009996964,0.004836989],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003214572,0.00005916919,0.0004792657,0.0002044923,0.00001472377,0.0009824893,0.008773174,0.0002831918,0.0003252444,0.8752901,0.0773809,0.03617506],"study_design_scores_gemma":[0.00004596515,0.0001071199,0.0008355404,0.001934819,0.00001708421,0.002415013,0.005122947,0.0004512628,0.0003075128,0.4152177,0.5734675,0.00007758953],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.004333911,0.03839136,0.01607453,0.8112622,0.01147153,0.0001888231,0.00005820582,0.00006747094,0.118152],"genre_scores_gemma":[0.2265899,0.03043383,0.04197161,0.6160834,0.0382977,0.001219263,0.0001115335,0.000165866,0.04512699],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1108285,"threshold_uncertainty_score":0.5861242,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00993393086178797,"score_gpt":0.3117568062898145,"score_spread":0.3018228754280265,"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."}}