{"id":"W3179622865","doi":"10.2139/ssrn.3863417","title":"Anti-selection & Genetic Testing in Insurance: An Interdisciplinary Perspective","year":2021,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Insurance and Financial Risk Management","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University; McGill University and Génome Québec Innovation Centre","funders":"","keywords":"Perspective (graphical); Genetic testing; Selection (genetic algorithm); Genetics; Biology; Computer science; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001198785,0.0001870446,0.0003736132,0.0003538259,0.0003274996,0.0001192144,0.0002361963,0.00009837829,0.00007228437],"category_scores_gemma":[0.0001984411,0.0002277117,0.0001159989,0.0008781913,0.00003709432,0.000450176,0.0001022738,0.001385067,0.0002680123],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001779134,"about_ca_system_score_gemma":0.0005088982,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004162727,"about_ca_topic_score_gemma":0.009691359,"domain_scores_codex":[0.9972652,0.00004993376,0.0006228983,0.0004725862,0.00006931217,0.00152008],"domain_scores_gemma":[0.9992172,0.00002453328,0.0003150158,0.0002276245,0.0001539158,0.0000617745],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00004782123,0.0003589458,0.6272623,0.0000145053,0.00008954515,0.00002876196,0.001617592,0.001723401,0.0002276725,0.3492717,0.00006163413,0.01929615],"study_design_scores_gemma":[0.000570568,0.0001230606,0.4225273,0.00002767061,0.000005196007,0.0002588604,0.001479644,0.0002874222,0.00001362848,0.572811,0.001629075,0.0002665912],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9642609,0.007737868,0.01928558,0.0004017631,0.0004026442,0.0001238584,0.00001027758,0.00002888706,0.007748174],"genre_scores_gemma":[0.993567,0.003465061,0.001369999,0.0000860067,0.0004010296,0.00001253324,0.000007012234,0.00003386835,0.001057415],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2235393,"threshold_uncertainty_score":0.9285812,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02830949779708145,"score_gpt":0.2759795349668899,"score_spread":0.2476700371698084,"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."}}