{"id":"W2946308425","doi":"10.1007/s11009-019-09723-7","title":"Bayesian Inference with M-splines on Spectral Measure of Bivariate Extremes","year":2019,"lang":"en","type":"article","venue":"Methodology And Computing In Applied Probability","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Agence Nationale de la Recherche","keywords":"Mathematics; Bivariate analysis; Measure (data warehouse); Bayesian probability; Inference; Bayesian inference; Frequentist inference; Econometrics; Statistics; Applied mathematics; Artificial intelligence; Data mining; Computer science","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":[0.003417474,0.0001548408,0.0006099835,0.0001246516,0.00004945647,0.00001086189,0.0001399962,0.0001488813,0.00002887287],"category_scores_gemma":[0.0002789404,0.0001466644,0.00003966126,0.0002208953,0.0001181722,0.00003139877,0.00006139689,0.0002862222,0.000008516047],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002990814,"about_ca_system_score_gemma":0.00002806642,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001876372,"about_ca_topic_score_gemma":0.00007078061,"domain_scores_codex":[0.9984825,0.0001211855,0.0005652475,0.0005509009,0.0000352228,0.0002449506],"domain_scores_gemma":[0.9986591,0.0007229653,0.0002499291,0.0003104495,0.00002494105,0.00003255843],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0003072048,0.00008100253,0.5968193,0.00009441592,0.00001109714,2.962607e-7,0.0006066865,0.006696531,0.00008884913,0.3894513,4.169655e-7,0.00584284],"study_design_scores_gemma":[0.000786695,0.0001956658,0.4337872,0.0000480115,0.000003947443,0.00000105053,0.00003825887,0.05332559,0.0003276486,0.511252,0.00003211457,0.0002018263],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7483523,0.0001298548,0.2469428,0.00006303107,0.00007897182,0.0002981329,0.000004583186,0.00001908407,0.004111157],"genre_scores_gemma":[0.8775194,0.000007946369,0.1223834,0.00004216085,0.00002237137,0.00000595994,0.000001657368,0.000008064892,0.000009049901],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1630321,"threshold_uncertainty_score":0.5980803,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1064774997835175,"score_gpt":0.2780772106486711,"score_spread":0.1715997108651536,"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."}}