{"id":"W3128789444","doi":"10.3390/risks9020035","title":"Mortality Forecasting with an Age-Coherent Sparse VAR Model","year":2021,"lang":"en","type":"article","venue":"Risks","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Autoregressive model; Vector autoregression; Econometrics; Context (archaeology); Term (time); Dimension (graph theory); Population; Computer science; Mortality rate; Statistics; Mathematics; Demography; Geography","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001011882,0.0001929689,0.0002565901,0.00006716217,0.000636904,0.0002059463,0.0003225823,0.00009883473,0.0001593007],"category_scores_gemma":[0.00007299541,0.0001805122,0.0001167494,0.0005037434,0.000285249,0.0003539895,0.00008910969,0.0002138512,0.00002351525],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009084054,"about_ca_system_score_gemma":0.0002026644,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.006820714,"about_ca_topic_score_gemma":0.04788116,"domain_scores_codex":[0.9975346,0.0003006374,0.0002801848,0.0005223044,0.0007918406,0.0005704643],"domain_scores_gemma":[0.9988442,0.00003818049,0.0001469079,0.0005598349,0.0001931321,0.0002177659],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006862061,0.0009542591,0.854776,0.0001151149,0.0003960405,0.001020014,0.01611608,0.02321656,0.00005245835,0.06916662,0.00143393,0.03268434],"study_design_scores_gemma":[0.001982037,0.0002759123,0.7997653,0.0001931407,0.0006356796,0.0000100223,0.01645701,0.1074482,0.0004089365,0.04921601,0.0217542,0.001853562],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9176223,0.0001057481,0.003972788,0.000148897,0.000240077,0.0003858744,0.00002138563,0.0001725769,0.07733033],"genre_scores_gemma":[0.9928876,0.0001111038,0.00541692,0.0002300303,0.0002387141,0.00005202507,0.00002486042,0.00002684556,0.001011964],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08423166,"threshold_uncertainty_score":0.9997929,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1985972197974165,"score_gpt":0.3745388889291356,"score_spread":0.1759416691317192,"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."}}