{"id":"W2269062277","doi":"10.1017/9781108784184.015","title":"Universal life insurance","year":2019,"lang":"en","type":"book-chapter","venue":"Cambridge University Press eBooks","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Life insurance; Actuarial science; Cash flow; Computer science; Finance; Economics","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":[0.0004338916,0.0005434147,0.0003710753,0.001715898,0.0007854204,0.00196691,0.0006028776,0.0008924609,0.1314442],"category_scores_gemma":[0.001582107,0.0002135297,0.0002936731,0.001379818,0.0006326947,0.002344207,0.001541842,0.001563528,0.03708442],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001403133,"about_ca_system_score_gemma":0.0008325363,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00173039,"about_ca_topic_score_gemma":0.002645305,"domain_scores_codex":[0.9996102,0.00003934767,0.00001584478,0.00007371519,0.0002154519,0.00004538665],"domain_scores_gemma":[0.9997136,0.00007360458,0.00001807943,0.0000707604,0.0000705559,0.00005338022],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001321679,0.00002636262,0.0002456737,0.000167652,0.000003170583,0.00005839957,0.0002935212,0.0003588869,0.0003276649,0.2634602,0.40398,0.3310652],"study_design_scores_gemma":[7.828058e-7,0.000007700793,0.000508813,0.000130949,0.000001153589,0.0001100199,0.00003361381,0.0001449902,0.00007209318,0.01403624,0.9849508,0.000002997769],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.001482757,0.01672196,0.00295809,0.002003021,0.00133307,0.00003419206,0.0007698681,0.0003584391,0.9743386],"genre_scores_gemma":[0.02037437,0.009664881,0.002772971,0.001279842,0.0007523572,0.00005330086,0.001219888,0.0001842196,0.9636981],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.1314442,"threshold_uncertainty_score":0.4397246,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02362667138400698,"score_gpt":0.2252805779846403,"score_spread":0.2016539066006333,"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."}}