{"id":"W2460748582","doi":"10.1007/s13385-016-0134-y","title":"Rank-based methods for modeling dependence between loss triangles","year":2016,"lang":"en","type":"article","venue":"European Actuarial Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; McGill University","funders":"Fonds de recherche du Québec – Nature et technologies; Canada Excellence Research Chairs, Government of Canada; Canadian Statistical Sciences Institute; Natural Sciences and Engineering Research Council of Canada; Mitacs; Canada Research Chairs","keywords":"Copula (linguistics); Econometrics; Portfolio; Inference; Multivariate statistics; Model selection; Computer science; Mathematics; Economics; Statistics; Finance; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01238304,0.0002230287,0.0003353362,0.0002484893,0.001272921,0.0003929309,0.000955968,0.00007786128,0.00016774],"category_scores_gemma":[0.001426517,0.0001612143,0.0004178835,0.0002437272,0.0002512331,0.00048344,0.00007468434,0.0002103725,0.00007179318],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000162201,"about_ca_system_score_gemma":0.0002224984,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009137301,"about_ca_topic_score_gemma":0.0000676685,"domain_scores_codex":[0.9946983,0.002963112,0.0006618552,0.0003623339,0.0006253486,0.0006889754],"domain_scores_gemma":[0.9979323,0.0008092683,0.0003719296,0.0002885583,0.0002822441,0.0003157021],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005792069,0.0001215572,0.01171217,0.00002426948,0.0003366247,0.0001069404,0.002805398,0.000524902,0.0007946338,0.01127008,0.002186959,0.9695373],"study_design_scores_gemma":[0.0308747,0.001025787,0.02383737,0.0009648755,0.001381368,0.0000373765,0.002863431,0.004558973,0.001795186,0.08834542,0.8409117,0.003403764],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0383547,0.0001166333,0.9444764,0.002596379,0.002189291,0.0005102762,0.00002199994,0.000121086,0.01161328],"genre_scores_gemma":[0.9448711,0.0001974787,0.04895686,0.0002342965,0.005332395,0.000007139024,0.00000231747,0.00005415692,0.0003442748],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9661335,"threshold_uncertainty_score":0.9790411,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07999562808320786,"score_gpt":0.3959160004885215,"score_spread":0.3159203724053137,"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."}}