{"id":"W4399817129","doi":"10.1080/03461238.2024.2365977","title":"Spatial natural hedging: a general framework with application to the mortality of U.S. states","year":2024,"lang":"en","type":"article","venue":"Scandinavian Actuarial Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Francis Xavier University","funders":"","keywords":"Natural (archaeology); Econometrics; Geography; Mathematics; Computer science; Statistics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001631296,0.000183017,0.0002337012,0.0001937678,0.0007507602,0.0005499844,0.0005856249,0.00008999868,0.00012621],"category_scores_gemma":[0.00008221588,0.0001184874,0.0001709016,0.0008126082,0.0003495736,0.0002742249,0.00006266276,0.0005915176,0.00002483985],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001925689,"about_ca_system_score_gemma":0.0002203181,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006189508,"about_ca_topic_score_gemma":0.005290837,"domain_scores_codex":[0.9975438,0.0002707104,0.0003920122,0.0002973142,0.001026978,0.00046922],"domain_scores_gemma":[0.9990475,0.0001050096,0.0001851058,0.0002957263,0.0001670663,0.0001996004],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00104118,0.0003446404,0.3501658,0.0001849976,0.001739186,0.0002204876,0.08061006,0.002700577,0.0003951949,0.2225003,0.01412294,0.3259746],"study_design_scores_gemma":[0.001201958,0.0005986068,0.7152399,0.0008204154,0.0006560485,0.00006413749,0.00810471,0.003024189,0.0003859407,0.08363232,0.1851705,0.001101164],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8823141,0.0006045092,0.1014095,0.0064303,0.005041626,0.001058653,0.00004350902,0.0001211238,0.00297663],"genre_scores_gemma":[0.9946837,0.0001682762,0.00123905,0.0002587642,0.003448132,0.00003002838,0.000008559417,0.00002136223,0.0001421538],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3650741,"threshold_uncertainty_score":0.9356726,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01007467276342909,"score_gpt":0.3132846967513995,"score_spread":0.3032100239879704,"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."}}