{"id":"W3125712333","doi":"10.1080/10920277.2020.1806884","title":"A DSA Algorithm for Mortality Forecasting","year":2020,"lang":"en","type":"article","venue":"North American Actuarial Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Feature selection; Mortality rate; Feature (linguistics); Population; Selection (genetic algorithm); Data mining; Artificial intelligence; Machine learning; Econometrics; Mathematics; Demography","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":[0.001792486,0.0008678044,0.001056469,0.001517107,0.0008697344,0.001132356,0.001423064,0.001116592,0.004542529],"category_scores_gemma":[0.00577464,0.0004752433,0.0008002445,0.001856335,0.0005809306,0.0011781,0.001398449,0.001729014,0.001878019],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007393686,"about_ca_system_score_gemma":0.001933488,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007094628,"about_ca_topic_score_gemma":0.005919346,"domain_scores_codex":[0.9991282,0.0003168132,0.00008412651,0.0002350172,0.0001830077,0.00005286952],"domain_scores_gemma":[0.9986746,0.0007222077,0.00007480774,0.0001133344,0.0003586398,0.00005650378],"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":[0.00007861566,0.00004753289,0.001588273,0.00007026761,0.00006647179,0.00006802568,0.00007467568,0.5384653,0.001522194,0.02721978,0.00574336,0.4250555],"study_design_scores_gemma":[0.000007701972,0.00001322204,0.00009991633,0.000006176398,0.000005885382,0.00002055542,0.000008543733,0.9877489,0.0002611914,0.009274741,0.002547586,0.000005629587],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002525962,0.0001957127,0.9958158,0.0001421944,0.0000645839,0.00003020169,0.00008306873,0.0002271195,0.0009153482],"genre_scores_gemma":[0.09785287,0.0004761207,0.8958903,0.0001914912,0.0001526151,0.000294632,0.0006563145,0.00009982155,0.004385816],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007094628,"threshold_uncertainty_score":0.01519626,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07551085995473171,"score_gpt":0.328622378801259,"score_spread":0.2531115188465273,"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."}}