{"id":"W1571156965","doi":"10.1002/9780470015902.a0005204.pub3","title":"Insurance and Human Genetics: Approaches to Regulation","year":2017,"lang":"en","type":"other","venue":"Encyclopedia of Life Sciences","topic":"Ethics and Legal Issues in Pediatric Healthcare","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Genetic discrimination; Genetic testing; Adverse selection; Context (archaeology); Selection (genetic algorithm); Business; Test (biology); Actuarial science; Public economics; Economics; Biology; Genetics; Computer 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009673647,0.000554272,0.0007974105,0.002276367,0.002682891,0.007783839,0.001680143,0.007805376,0.008837041],"category_scores_gemma":[0.009809127,0.0003882343,0.00100573,0.00134058,0.0389068,0.006849179,0.003873033,0.00742678,0.0009273024],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007962085,"about_ca_system_score_gemma":0.003784923,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008637581,"about_ca_topic_score_gemma":0.003740163,"domain_scores_codex":[0.9936417,0.003798532,0.0002146534,0.0008748621,0.001065391,0.0004049886],"domain_scores_gemma":[0.9897571,0.007700277,0.00048338,0.0008258612,0.0009831908,0.0002501623],"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":[0.000001699315,0.000003875212,0.00004847748,0.000007780406,0.000001859157,0.0000160548,0.0001562157,0.0001767298,0.00001809037,0.9976633,0.0007222979,0.001183715],"study_design_scores_gemma":[0.000005939588,0.000006191574,0.0001011628,0.00006010046,0.000002965049,0.00002449598,0.0001449718,0.0006089938,0.0000338768,0.9775991,0.02140494,0.000007349956],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.009968814,0.03015983,0.1414057,0.2033059,0.002730015,0.0001575552,0.0001816827,0.0001582574,0.6119322],"genre_scores_gemma":[0.8007279,0.01847991,0.04800657,0.04963725,0.006294789,0.0009160942,0.0001442707,0.0001329014,0.07566033],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.009673647,"threshold_uncertainty_score":0.0577693,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1344694622022417,"score_gpt":0.3663598413337433,"score_spread":0.2318903791315016,"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."}}