{"id":"W1582278691","doi":"10.1080/10920277.2010.10597597","title":"Developing Mortality Improvement Formulas","year":2010,"lang":"en","type":"article","venue":"North American Actuarial Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":44,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Society of Actuaries","keywords":"Annuity; Actuarial science; Life annuity; Pension; Valuation (finance); Longevity risk; Life insurance; Economics; Econometrics; Heuristic; Scale (ratio); Population; Mathematics; Finance; Geography; Demography; Sociology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.001718178,0.0002590737,0.0003755059,0.0002317168,0.001520495,0.0004626398,0.0007846786,0.00007103748,0.0003014158],"category_scores_gemma":[0.0002897367,0.0002411448,0.0002668562,0.0008105414,0.0008860888,0.0004938926,0.0001158094,0.0009333772,0.0000547663],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002342866,"about_ca_system_score_gemma":0.0007570822,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.01195163,"about_ca_topic_score_gemma":0.1155999,"domain_scores_codex":[0.9966393,0.0001851964,0.0006321373,0.0003544573,0.001258028,0.0009309521],"domain_scores_gemma":[0.9980909,0.00008126273,0.0006935358,0.0003693888,0.0003085665,0.000456362],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004461135,0.000104178,0.6394312,0.000007365444,0.0002125575,0.00004887432,0.002385653,0.00001267776,0.0001595559,0.008344367,0.001040074,0.3482089],"study_design_scores_gemma":[0.0005455017,0.0001383532,0.8407024,0.000007174136,0.0000783373,0.000008673625,0.001802549,0.00002034563,0.00008073626,0.001878507,0.1542871,0.000450408],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9789971,0.000009041943,0.004418315,0.001175161,0.004315971,0.0004137392,0.000011075,0.0001112632,0.01054841],"genre_scores_gemma":[0.9910496,0.0002507585,0.004296338,0.001089526,0.003167342,0.00001904736,0.000006248549,0.00002596438,0.00009513648],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3477585,"threshold_uncertainty_score":0.9997794,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0235352521633095,"score_gpt":0.3303506855262878,"score_spread":0.3068154333629783,"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."}}