{"id":"W3121682060","doi":"10.2139/ssrn.3720332","title":"Maximum Spectral Measures of Risk with Given Risk Factor Marginal Distributions","year":2020,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Mathematics; Measure (data warehouse); Wasserstein metric; Risk measure; Upper and lower bounds; Mathematical optimization; Applied mathematics; Duality (order theory); Expected shortfall; Constraint (computer-aided design); Metric (unit); Function (biology); Mathematical analysis; Combinatorics; Risk management; Computer science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007402535,0.001875718,0.001634302,0.002710607,0.0006521179,0.004454256,0.002181516,0.002982998,0.005844805],"category_scores_gemma":[0.04866149,0.0009001614,0.0014467,0.001753311,0.003436977,0.008494547,0.003339133,0.002538509,0.000739258],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001777816,"about_ca_system_score_gemma":0.0009369748,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003337144,"about_ca_topic_score_gemma":0.0002574425,"domain_scores_codex":[0.996473,0.001867872,0.0001391227,0.0005961534,0.0006531655,0.0002706339],"domain_scores_gemma":[0.972408,0.02211028,0.001676752,0.001340745,0.001576649,0.0008875038],"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":[0.0001122511,0.00005902019,0.0004937216,0.0001890578,0.00006079411,0.00005818635,0.0001628101,0.05837516,0.001197424,0.9240377,0.001230801,0.01402309],"study_design_scores_gemma":[0.00001692568,0.00003403934,0.0004568595,0.00005102333,0.00002046598,0.00008378929,0.00004645151,0.1841504,0.0006080201,0.8137271,0.0007743133,0.00003057931],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05801718,0.0007377955,0.92952,0.000988599,0.00007868669,0.00005642685,0.00027175,0.0002020895,0.01012763],"genre_scores_gemma":[0.832046,0.001863815,0.1548936,0.0003499528,0.0006110909,0.0003894263,0.000420023,0.0003026888,0.009123404],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007402535,"threshold_uncertainty_score":0.03914881,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04752477288594668,"score_gpt":0.3116074714124667,"score_spread":0.26408269852652,"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."}}