{"id":"W4402407033","doi":"10.1080/00031305.2024.2402898","title":"When Heavy Tails Disrupt Statistical Inference","year":2024,"lang":"en","type":"article","venue":"The American Statistician","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Statistical inference; Inference; Econometrics; Computer science; Statistics; Mathematics; Statistical physics; Artificial intelligence; Physics","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000500641,0.0001789199,0.0004150652,0.0001143763,0.000175769,0.0002271893,0.0002916104,0.00002791222,0.0004365759],"category_scores_gemma":[0.0004852312,0.0001540429,0.00006495925,0.0003218502,0.0004450837,0.0001349139,0.00007371856,0.0002867235,0.001530012],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008895768,"about_ca_system_score_gemma":0.0000633957,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004652467,"about_ca_topic_score_gemma":0.0002240944,"domain_scores_codex":[0.9984975,0.00004055915,0.0005321571,0.0004436159,0.0000703062,0.000415828],"domain_scores_gemma":[0.9986528,0.0006914859,0.000152613,0.0003756989,0.00002521734,0.0001021951],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00002868204,0.00002569834,0.004642788,0.00002925107,0.00003235956,0.00002213779,0.001355833,0.00005557896,0.000004683047,0.9121287,0.005083185,0.0765911],"study_design_scores_gemma":[0.0001049711,0.0002036766,0.05216502,0.00004149649,0.00002167382,0.000004771562,0.000289668,0.152432,0.000006674395,0.7212689,0.07307912,0.0003820263],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06688552,0.001437239,0.9202366,0.001850283,0.0004298973,0.0001835742,0.00209073,0.0001351275,0.006751055],"genre_scores_gemma":[0.9867421,0.0001444769,0.01171242,0.0006354717,0.0001848248,0.00002737129,0.00003576248,0.00003443752,0.0004831708],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9198565,"threshold_uncertainty_score":0.9992474,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03880810499610163,"score_gpt":0.2962289158894527,"score_spread":0.2574208108933511,"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."}}