{"id":"W3092314949","doi":"10.3390/risks8040103","title":"Grouped Normal Variance Mixtures","year":2020,"lang":"en","type":"article","venue":"Risks","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Copula (linguistics); Mathematics; Multivariate normal distribution; Multivariate statistics; Variance (accounting); Monte Carlo method; Statistics; Mixing (physics); Multivariate t-distribution; Applied mathematics; Econometrics","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000176646,0.0001053364,0.0002444887,0.00003712772,0.0000937251,0.00004160948,0.0001826344,0.00009009697,0.000290738],"category_scores_gemma":[0.0001712713,0.0001258987,0.00008989179,0.0001664017,0.00002318241,0.000169522,0.00004912113,0.0001804628,0.001030332],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000189004,"about_ca_system_score_gemma":0.00001135971,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004624401,"about_ca_topic_score_gemma":0.00001125983,"domain_scores_codex":[0.9990747,0.00000748465,0.0003510972,0.0003137435,0.00002658491,0.0002264136],"domain_scores_gemma":[0.9995738,0.00002164744,0.0001173694,0.0001716947,0.00001568142,0.00009978286],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001413499,0.0001160895,0.2483745,0.00009532872,0.00004660667,0.00001927794,0.00313937,0.00211131,0.000250136,0.7228593,0.005695067,0.01715171],"study_design_scores_gemma":[0.001667001,0.0002640317,0.1809259,0.00002248024,0.00001620179,0.000003380824,0.00007234713,0.3453835,0.0007782809,0.1534731,0.3163682,0.001025568],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6117809,0.00656345,0.3443817,0.003608548,0.0006254477,0.0002417098,0.0001936422,0.00018146,0.03242318],"genre_scores_gemma":[0.9957354,0.0002063116,0.002414545,0.001152752,0.0003519775,0.000007033755,0.000008379449,0.00001608434,0.0001075383],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5693862,"threshold_uncertainty_score":0.9997475,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1129438916127381,"score_gpt":0.2579721307069157,"score_spread":0.1450282390941776,"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."}}