{"id":"W982962200","doi":"","title":"Genetics and life insurance in Canada: points to consider","year":2003,"lang":"en","type":"article","venue":"Érudit documents and data repository (Érudit Consortium, University of Montreal)","topic":"Healthcare Policy and Management","field":"Economics, Econometrics and Finance","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Institutes of Health Research; Ontario Genomics; Ontario Genomics Institute; Genome Canada","keywords":"Life insurance; Computer science; Data science; Computational biology; Biology; Actuarial science; Business","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002315436,0.0003464403,0.000797865,0.00319974,0.006400124,0.006723206,0.002010302,0.005498294,0.0100664],"category_scores_gemma":[0.01071647,0.0002329232,0.0008622141,0.008095958,0.003272597,0.002444494,0.002270578,0.003266095,0.0002652437],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.09038997,"about_ca_system_score_gemma":0.2083647,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9932194,"about_ca_topic_score_gemma":0.9961785,"domain_scores_codex":[0.9969175,0.0003820055,0.00009278476,0.0001933996,0.0007464141,0.001667851],"domain_scores_gemma":[0.9898421,0.001837866,0.0006645703,0.0001827115,0.004416269,0.003056376],"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":[0.0003453712,0.0001437698,0.2954678,0.0005413526,0.0003211381,0.004512087,0.01069399,0.003750848,0.000331897,0.4242633,0.1580207,0.1016077],"study_design_scores_gemma":[0.0001099524,0.00008450869,0.4164965,0.002921985,0.0006323249,0.00158177,0.0746796,0.003463723,0.0003416813,0.1142759,0.3851151,0.0002968214],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.1020049,0.05047989,0.0009081257,0.768409,0.0007677207,0.00004792647,0.002534474,0.00003410398,0.07481385],"genre_scores_gemma":[0.8863142,0.03768361,0.001499796,0.04547095,0.0009564107,0.00004271551,0.0009572831,0.00003548068,0.02703957],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.09038997,"threshold_uncertainty_score":0.6558282,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02916809258224407,"score_gpt":0.2243862454055274,"score_spread":0.1952181528232833,"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."}}