{"id":"W6920520721","doi":"10.6084/m9.figshare.13088990.v1","title":"The GLM framework of the Lee–Carter model: a multi-country study","year":2020,"lang":"en","type":"article","venue":"Figshare","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Deviance (statistics); Generalized linear model; Negative binomial distribution; Log-linear model; Quasi-likelihood; Statistical model; Linear model; Count data","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002461926,0.0001204837,0.0001408551,0.00001622701,0.0007457166,0.0001285946,0.00102114,0.0000812052,0.003886508],"category_scores_gemma":[0.001017636,0.00007472392,0.0001431722,0.0005551527,0.00007146959,0.0001091589,0.0002597224,0.0002665033,0.0001724601],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002564129,"about_ca_system_score_gemma":0.00008662615,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001655413,"about_ca_topic_score_gemma":0.001448277,"domain_scores_codex":[0.9983199,0.0002304029,0.0002227371,0.0002397716,0.0006928743,0.0002943745],"domain_scores_gemma":[0.9990546,0.0001608971,0.0001499847,0.0004366549,0.0001270894,0.00007074747],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006442582,0.0008520673,0.08977231,0.0002533743,0.0004819783,0.00002066646,0.1647014,0.003205594,0.000007631817,0.00819314,0.7228214,0.009626033],"study_design_scores_gemma":[0.00107781,0.0001560427,0.4157945,0.0007733867,0.0001389858,2.110765e-7,0.04396844,0.01897869,0.00003477214,0.002668018,0.5156856,0.0007236299],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.3228514,0.006585393,0.0005627255,0.04401762,0.004570342,0.03184229,0.3854282,0.002212378,0.2019297],"genre_scores_gemma":[0.9980433,0.00001056432,0.00007396888,0.001147069,0.0001974008,0.0001674201,0.0001550093,0.00001540413,0.0001898401],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6751919,"threshold_uncertainty_score":0.9970241,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05972128973536565,"score_gpt":0.3285793857727753,"score_spread":0.2688580960374096,"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."}}