{"id":"W1971966300","doi":"10.1016/j.matcom.2010.04.025","title":"Modeling old-age mortality risk for the populations of Australia and New Zealand: An extreme value approach","year":2010,"lang":"en","type":"article","venue":"Mathematics and Computers in Simulation","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo; Actua","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Life table; Demography; Mortality rate; Population; Table (database); Centenarian; Raw data; Geography; Statistics; Computer science; Mathematics; Sociology","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":[0.002248051,0.0007263768,0.001302224,0.000877602,0.0006357015,0.001793002,0.002789365,0.001791646,0.001302252],"category_scores_gemma":[0.007576829,0.0007561982,0.001506891,0.0007404619,0.0013052,0.001790008,0.001628363,0.002016617,0.00009941953],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002350413,"about_ca_system_score_gemma":0.00179528,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.08398856,"about_ca_topic_score_gemma":0.04113206,"domain_scores_codex":[0.9992867,0.000364789,0.00003739485,0.0001213821,0.0000741932,0.0001154942],"domain_scores_gemma":[0.9981767,0.001115727,0.000238414,0.00007438702,0.00019927,0.0001954348],"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":[0.00002086901,0.00002955323,0.004515155,0.000008869267,0.00005568922,0.00007627402,0.00008253915,0.9868607,0.00008224935,0.006336063,0.0001305307,0.001801588],"study_design_scores_gemma":[0.000006406542,0.0000121733,0.0007551109,0.000002577881,0.00001262474,0.00001300815,0.00003287275,0.9939378,0.00002377789,0.005113107,0.00008359694,0.000006885734],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5564594,0.000458174,0.4375526,0.001718757,0.00008689587,0.00009962604,0.00035423,0.000120119,0.003150195],"genre_scores_gemma":[0.9800741,0.0002494811,0.01660401,0.00008033471,0.00003804208,0.00009251337,0.0001894262,0.0000241818,0.00264779],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08398856,"threshold_uncertainty_score":0.1669993,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1656828185347755,"score_gpt":0.374494994165238,"score_spread":0.2088121756304625,"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."}}