{"id":"W2187581550","doi":"10.2139/ssrn.2617350","title":"Lifetime Ruin Under Uncertain Hazard Rate","year":2015,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Economics; Actuarial 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":[0.005211197,0.0007003386,0.002034914,0.001317636,0.0006627265,0.002178414,0.001534003,0.001859224,0.006161002],"category_scores_gemma":[0.02365681,0.0005559519,0.0008415374,0.0008562665,0.00156568,0.002999307,0.00149567,0.001785764,0.0004043005],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001054895,"about_ca_system_score_gemma":0.0007268694,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00434138,"about_ca_topic_score_gemma":0.001775753,"domain_scores_codex":[0.9983814,0.0004650014,0.00006855068,0.0002932279,0.0001430363,0.0006487276],"domain_scores_gemma":[0.9694319,0.02352507,0.00390897,0.0009612543,0.0007697901,0.001402985],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001843346,0.0003794221,0.08729484,0.0005346116,0.0004514882,0.004723626,0.001540827,0.676082,0.003412977,0.1881126,0.006295457,0.02932882],"study_design_scores_gemma":[0.0000564465,0.0002768581,0.01773529,0.00004493822,0.0001574457,0.001001139,0.0004852327,0.9208828,0.0004249607,0.05825415,0.0006062248,0.00007455074],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.889672,0.001665409,0.09654739,0.002807881,0.0001408164,0.00008197362,0.001501135,0.0003226903,0.007260626],"genre_scores_gemma":[0.9958757,0.0002255756,0.0006697645,0.00003836463,0.00005333141,0.00001685667,0.0001456962,0.00001567257,0.002959128],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006161002,"threshold_uncertainty_score":0.02755976,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02706839816283165,"score_gpt":0.3094915146190526,"score_spread":0.2824231164562209,"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."}}