{"id":"W3156919873","doi":"10.1016/j.insmatheco.2021.03.018","title":"Gompertz law revisited: Forecasting mortality with a multi-factor exponential model","year":2021,"lang":"en","type":"article","venue":"Insurance Mathematics and Economics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":16,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Gompertz function; Life expectancy; Exponential function; Econometrics; Estimation; Exponential smoothing; Focus (optics); Computer science; Statistics; Population; Mathematics; Demography; Economics","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":[0.005655994,0.0007203885,0.001835987,0.00103078,0.0005183195,0.002583592,0.003007901,0.004438232,0.001466734],"category_scores_gemma":[0.0333495,0.0006670032,0.000962765,0.001225647,0.001613082,0.004773217,0.001014737,0.003055066,0.0002508515],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001424218,"about_ca_system_score_gemma":0.0009790359,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02325875,"about_ca_topic_score_gemma":0.01054875,"domain_scores_codex":[0.9992017,0.0003295955,0.00004628735,0.000206022,0.0001153043,0.0001010613],"domain_scores_gemma":[0.9901884,0.007864299,0.0007280848,0.0003904408,0.0005558811,0.0002728944],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009932613,0.00006219875,0.01273535,0.00006481582,0.00008950829,0.0002776509,0.0001626281,0.9009522,0.000357228,0.06921541,0.001495556,0.01448815],"study_design_scores_gemma":[0.000006808905,0.000007963378,0.0008196558,0.000008571875,0.00001034755,0.00002120493,0.0000170293,0.9831703,0.00003829522,0.01577804,0.0001122424,0.000009467104],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5872105,0.00335961,0.3916923,0.01238081,0.0004227433,0.00004404047,0.0004271831,0.0002269887,0.00423574],"genre_scores_gemma":[0.9879524,0.001100872,0.007815901,0.0002375176,0.0002624458,0.00001720383,0.000123149,0.00002109277,0.002469439],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02325875,"threshold_uncertainty_score":0.04624671,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07905345941122027,"score_gpt":0.2904700243175524,"score_spread":0.2114165649063321,"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."}}