{"id":"W1747210889","doi":"10.29173/alr97","title":"Potential for Genetic Discrimination in Access to Insurance: Is There a Dark Side to Increased Availability of Genetic Information?","year":2013,"lang":"en","type":"article","venue":"Alberta Law Review","topic":"Legal Systems and Judicial Processes","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Underwriting; Genetic discrimination; Actuarial science; Legislature; Medical underwriting; Business; Population; Private information retrieval; Genetic testing; Insurance policy; General insurance; Political science; Law; Medicine; Income protection insurance; Computer security; Computer science; Environmental health","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004315897,0.0001090913,0.0003305912,0.00004206739,0.0001385967,0.000127022,0.0003847428,0.00005766418,0.0002614524],"category_scores_gemma":[0.00090342,0.00009162883,0.00007433422,0.0004918596,0.00005993153,0.0008130168,0.0000600262,0.00004202959,0.0001391581],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005869879,"about_ca_system_score_gemma":0.0001441446,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.4229808,"about_ca_topic_score_gemma":0.1047833,"domain_scores_codex":[0.9985368,0.0001438465,0.0006274107,0.0001768784,0.0002878332,0.000227194],"domain_scores_gemma":[0.9989455,0.0001539224,0.0001831915,0.0002175284,0.0003652859,0.0001346111],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.000162116,0.000727204,0.3001233,0.04848116,0.0001466878,0.000002035518,0.05068896,0.0002096124,0.000403787,0.04820516,0.05673828,0.4941117],"study_design_scores_gemma":[0.0004592357,0.0001368782,0.6498848,0.003885043,0.00007032415,7.624011e-7,0.0003286861,0.00002795119,0.000154856,0.005664022,0.3389698,0.000417578],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9458621,0.008672569,0.0003164203,0.01041319,0.0002043387,0.00746005,0.00003592043,0.00001797405,0.02701746],"genre_scores_gemma":[0.9928985,0.001440608,0.0003270236,0.004549665,0.00006380548,0.0006299522,0.000006080403,0.000007191928,0.00007713289],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4936941,"threshold_uncertainty_score":0.9115521,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0182991260174569,"score_gpt":0.3120985351544801,"score_spread":0.2937994091370232,"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."}}