{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002022444,0.0004780097,0.0004433704,0.002015021,0.0004953749,0.003103141,0.000568554,0.001907364,0.02085028],"category_scores_gemma":[0.003243912,0.0001370319,0.0006144558,0.001826346,0.001635147,0.001793521,0.001057728,0.001293811,0.000428177],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001965318,"about_ca_system_score_gemma":0.001535606,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009287587,"about_ca_topic_score_gemma":0.0103829,"domain_scores_codex":[0.9989815,0.0005481314,0.00001353169,0.0000628957,0.0002968562,0.00009697217],"domain_scores_gemma":[0.9968649,0.00231646,0.0002369312,0.00008240656,0.0002892468,0.0002100724],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0003276463,0.0005438006,0.0269218,0.0003297044,0.0003030415,0.0008088424,0.0002239856,0.05658013,0.001371464,0.8274541,0.02244926,0.06268623],"study_design_scores_gemma":[0.0001574838,0.0005672027,0.04564496,0.0004926617,0.0002228063,0.0004311788,0.001820311,0.06013009,0.0008445293,0.8384869,0.05113035,0.0000714906],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.2461738,0.03130717,0.01969489,0.07007303,0.00055059,0.0001673407,0.003170972,0.00008621322,0.628776],"genre_scores_gemma":[0.9767416,0.008240554,0.002391135,0.002336418,0.0005079065,0.0000453504,0.000224227,0.00001071828,0.009502152],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.02085028,"threshold_uncertainty_score":0.06975108,"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."}}