{"id":"W1499840552","doi":"10.1002/9780470015902.a0005206","title":"Insurance and Human Genetics: Insurance Market Perspective","year":2008,"lang":"en","type":"other","venue":"Encyclopedia of Life Sciences","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Perspective (graphical); Life insurance; Actuarial science; Disability insurance; Health insurance; Business; Economics; Health care; Computer science; Economic growth","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":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.00134976,0.0004352133,0.0007016248,0.0007138579,0.0009677833,0.00006988689,0.001255517,0.0003111244,0.0008652792],"category_scores_gemma":[0.0003514905,0.0004223758,0.0001715943,0.001211026,0.006633233,0.0002451834,0.0001946442,0.0002890922,0.00002629058],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006727821,"about_ca_system_score_gemma":0.0005026431,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02238469,"about_ca_topic_score_gemma":0.01453991,"domain_scores_codex":[0.9956496,0.0004656967,0.0005580434,0.001011401,0.001610762,0.000704493],"domain_scores_gemma":[0.9981759,0.0001591263,0.0007531527,0.0004679026,0.0001730507,0.0002709356],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000007711505,0.0001186549,0.6572675,0.0001033923,0.00008612921,0.00001216535,0.005316754,0.000003212422,0.000001329952,0.01722763,0.318373,0.001482542],"study_design_scores_gemma":[0.0002533124,0.00008501845,0.3632281,0.0001509149,0.00002003487,9.030937e-7,0.003328782,0.000001607198,0.000001041508,0.001923532,0.6305138,0.0004929124],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.02791908,0.01898648,0.000008536851,0.0002609045,0.001165158,0.0008010937,0.000140653,0.0001813732,0.9505367],"genre_scores_gemma":[0.4116624,0.1840978,0.002244362,0.0003046915,0.002039177,0.00009475664,0.00000536957,0.0002279118,0.3993235],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.5512132,"threshold_uncertainty_score":0.9998228,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0178361162976836,"score_gpt":0.3019201777310351,"score_spread":0.2840840614333515,"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."}}