{"id":"W3105408634","doi":"10.1007/s10107-024-02128-6","title":"Optimizing distortion riskmetrics with distributional uncertainty","year":2024,"lang":"en","type":"article","venue":"Mathematical Programming","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Distortion (music); Mathematical optimization; Applied mathematics; Econometrics; 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":[0.001977471,0.0001627382,0.0002388216,0.00027172,0.0001992644,0.001026459,0.0002728795,0.00008130979,0.0003026921],"category_scores_gemma":[0.00148695,0.00009816579,0.0001202995,0.002029622,0.0001360322,0.0004034885,0.00006260521,0.0001997285,0.0004164701],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001061522,"about_ca_system_score_gemma":0.00007428035,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005409524,"about_ca_topic_score_gemma":0.000003276027,"domain_scores_codex":[0.9971715,0.00007351085,0.0005932383,0.0004492265,0.001395856,0.0003166383],"domain_scores_gemma":[0.9981698,0.001038505,0.0001111672,0.000316462,0.0002121901,0.0001518807],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003884056,0.0001836844,0.001048313,0.00005638842,0.00004938706,0.00007492019,0.001080356,0.01335032,0.00001349696,0.1514616,0.001829377,0.8308133],"study_design_scores_gemma":[0.0003407524,0.0002491206,0.0004224735,0.0002471256,0.0001195966,0.0001340419,0.000885658,0.5561844,0.0001294543,0.2641488,0.1765788,0.0005596881],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02057678,0.0003867702,0.9748682,0.0003830274,0.0001809438,0.0002638594,0.00001367491,0.0002522263,0.003074457],"genre_scores_gemma":[0.913874,0.0000214201,0.08491702,0.00001874116,0.0001204519,0.00004718561,0.00004740076,0.00001981378,0.0009339292],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8932973,"threshold_uncertainty_score":0.9898164,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06577574395816546,"score_gpt":0.364925118855046,"score_spread":0.2991493748968805,"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."}}