{"id":"W4380079374","doi":"10.32920/23467526.v1","title":"Empirical Estimation of the Haezendonck-Goovaerts Risk","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Reservoir Engineering and Simulation Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Measure (data warehouse); Risk measure; Expected shortfall; Coherent risk measure; Estimation; Spectral risk measure; Empirical measure; Portfolio optimization; Econometrics; Convergence (economics); Value at risk; Estimator; Dynamic risk measure; Computer science; Empirical research; Mathematics; Statistics; Portfolio; Mathematical optimization; Risk management; Data mining; Economics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0004066991,0.0001863448,0.0002613375,0.000101083,0.00003051075,0.00002609236,0.0003166164,0.0002431672,0.00003928944],"category_scores_gemma":[0.0003763649,0.0001340636,0.0001870127,0.0002152694,0.00002285093,0.00002772075,0.0002449275,0.0005628499,0.00002578175],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005078604,"about_ca_system_score_gemma":0.00002511472,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005219934,"about_ca_topic_score_gemma":0.000006462121,"domain_scores_codex":[0.9989684,0.00008659618,0.0003618378,0.0001727297,0.0002599267,0.0001505338],"domain_scores_gemma":[0.9989007,0.0002915965,0.000066898,0.0006583746,0.00004015371,0.00004234539],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000001000583,0.000003408994,0.002269711,0.0001907982,0.00004421976,3.823896e-7,0.0001022901,0.9923802,0.00001812548,0.00003131676,0.003692814,0.001265749],"study_design_scores_gemma":[0.00009824732,0.000003036568,0.02291599,0.0000752863,0.00002687034,5.103784e-7,0.000005616577,0.9718982,0.001366875,0.002779854,0.0006940379,0.0001354732],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3359182,0.0001131321,0.65896,0.0001129095,0.001518954,0.0002345917,0.00003065588,0.0009367545,0.002174826],"genre_scores_gemma":[0.9320744,0.00009986545,0.06609012,0.000006700802,0.00009998414,0.00003110094,0.00001991579,0.00007322811,0.001504668],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5961562,"threshold_uncertainty_score":0.5466954,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05781369501387726,"score_gpt":0.3377235342509617,"score_spread":0.2799098392370845,"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."}}