{"id":"W4214481202","doi":"10.1038/npg.els.0005204","title":"Insurance and Human Genetics: Approaches to Regulation","year":2006,"lang":"en","type":"other","venue":"Encyclopedia of Life Sciences","topic":"Biomedical Ethics and Regulation","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Genetic discrimination; Biology; Genetic testing; Genetics","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":[0.005412683,0.0005403092,0.0005318077,0.002152843,0.002196037,0.008933838,0.001200317,0.007381894,0.01363049],"category_scores_gemma":[0.006078421,0.0002244185,0.000461112,0.002040506,0.0194538,0.005367788,0.003302818,0.005759455,0.001332007],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005931705,"about_ca_system_score_gemma":0.00300109,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006060003,"about_ca_topic_score_gemma":0.00410765,"domain_scores_codex":[0.9952356,0.002865913,0.0001339467,0.0004702718,0.001052945,0.0002413668],"domain_scores_gemma":[0.9955679,0.003164893,0.0002502542,0.0003654257,0.0004993776,0.0001521117],"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":[9.388756e-7,0.000003491495,0.00001912646,0.000007386618,7.28716e-7,0.00001334726,0.0001118502,0.00009810858,0.00001253465,0.9962317,0.001659571,0.001841264],"study_design_scores_gemma":[0.000005044587,0.000005321661,0.0001247407,0.000130879,0.000002897291,0.00005228004,0.0002396683,0.0005563868,0.00005387732,0.9140369,0.0847856,0.000006374432],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.002306912,0.01507486,0.0262742,0.05560157,0.0007410609,0.00004264635,0.00006493081,0.00005139708,0.8998424],"genre_scores_gemma":[0.6418732,0.03068207,0.02899232,0.03771948,0.004386609,0.0006512289,0.0001349184,0.0001348891,0.2554253],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.01363049,"threshold_uncertainty_score":0.04559851,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06531872438139112,"score_gpt":0.2936912118344231,"score_spread":0.228372487453032,"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."}}