{"id":"W2297405967","doi":"10.1017/cbo9780511800146","title":"Actuarial Mathematics for Life Contingent Risks","year":2009,"lang":"en","type":"book","venue":"Cambridge University Press eBooks","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":167,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Rigour; Intuition; Computer science; Perspective (graphical); Management science; Scale (ratio); Engineering ethics; Engineering; Psychology; Artificial intelligence; Mathematics; Cognitive science","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":[0.0005759035,0.0006797545,0.0003584756,0.0009693668,0.000782467,0.00235825,0.000589399,0.0008904011,0.02234074],"category_scores_gemma":[0.00259109,0.0003366043,0.0004930254,0.001018658,0.002046664,0.003278299,0.001092033,0.003252641,0.008319589],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001484544,"about_ca_system_score_gemma":0.0009480778,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001109467,"about_ca_topic_score_gemma":0.001069903,"domain_scores_codex":[0.9996617,0.00006792756,0.00001378343,0.0000411641,0.0001941592,0.00002123719],"domain_scores_gemma":[0.999468,0.0002982978,0.00004412456,0.00006876616,0.00008446629,0.00003640914],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000002364233,0.000007464529,0.00005755056,0.00003838999,0.000003178588,0.0000291279,0.0001136664,0.001707357,0.00009664901,0.9217927,0.05256805,0.02358349],"study_design_scores_gemma":[0.000002004529,0.000006388454,0.0001818344,0.00008184181,0.000002346141,0.00008922366,0.00004229001,0.003558135,0.00005908335,0.6787327,0.3172384,0.000005840244],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.003414122,0.02994793,0.1324924,0.01469444,0.002848698,0.00004357368,0.0003348724,0.000383577,0.8158404],"genre_scores_gemma":[0.1614211,0.05031019,0.0601243,0.004286621,0.004881401,0.0002217458,0.0006687344,0.0004540027,0.7176318],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.02234074,"threshold_uncertainty_score":0.07473725,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0589768037649738,"score_gpt":0.2834134196989687,"score_spread":0.2244366159339949,"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."}}