{"id":"W3125250180","doi":"10.2139/ssrn.3283190","title":"Optimal Allocation to Deferred Income Annuities","year":2018,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Global Health Care Issues","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Bequest; Asset allocation; Economics; Resource allocation; Annuity; Microeconomics; Optimal allocation; Proxy (statistics); Population; Asset (computer security); Time allocation; Actuarial science; Computer science; Finance; Pension; Life annuity; Portfolio","routes":{"ca_aff":true,"ca_fund":true,"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":[0.004523681,0.001253481,0.00282325,0.001365158,0.0008746706,0.004277693,0.002506265,0.003517804,0.02224767],"category_scores_gemma":[0.01998876,0.001136838,0.0008466653,0.001134516,0.001624075,0.003082656,0.002256898,0.003234515,0.001711506],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002619161,"about_ca_system_score_gemma":0.002868698,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002811261,"about_ca_topic_score_gemma":0.001708994,"domain_scores_codex":[0.9975407,0.001170541,0.0000750965,0.0003156544,0.0002204151,0.0006775128],"domain_scores_gemma":[0.993806,0.003821812,0.0003195434,0.0004742539,0.0005818566,0.0009966545],"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":[0.003353482,0.001155395,0.001866711,0.0006213815,0.0001705683,0.0004216683,0.0005507963,0.2721119,0.002431921,0.543915,0.03577893,0.1376223],"study_design_scores_gemma":[0.0008268153,0.0004956638,0.001994626,0.0002169804,0.00015017,0.0001803279,0.000666888,0.389882,0.001060988,0.5901721,0.01428941,0.00006407185],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4330065,0.002925995,0.3334379,0.02086605,0.001622433,0.001183315,0.001658387,0.001054554,0.204245],"genre_scores_gemma":[0.9455501,0.0006146896,0.02120651,0.0005928044,0.0004358432,0.0002568083,0.0001432368,0.0001124193,0.03108749],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02224767,"threshold_uncertainty_score":0.07442588,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03872886997720896,"score_gpt":0.4273048929823334,"score_spread":0.3885760230051244,"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."}}