{"id":"W2768676031","doi":"10.1287/opre.2020.2041","title":"Calibration of Distributionally Robust Empirical Optimization Models","year":2017,"lang":"en","type":"preprint","venue":"Operations Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"Japan Society for the Promotion of Science; Natural Sciences and Engineering Research Council of Canada; Ministry of Education, India","keywords":"Robustness (evolution); Resampling; Sample size determination; Mathematics; Statistics; Econometrics; Mathematical optimization; 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":["sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00761357,0.0002399613,0.0005021476,0.0009761584,0.00132226,0.00221321,0.001960501,0.0005703196,0.0006211777],"category_scores_gemma":[0.00655241,0.0001996364,0.0002037564,0.0008560604,0.000481473,0.001219278,0.001332082,0.0009633475,0.00005106712],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001758383,"about_ca_system_score_gemma":0.00232312,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005097643,"about_ca_topic_score_gemma":0.0002670987,"domain_scores_codex":[0.9919192,0.001253473,0.001332205,0.0009642419,0.004138684,0.0003922434],"domain_scores_gemma":[0.9901489,0.0006524589,0.0003012664,0.002179387,0.006519279,0.0001986873],"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":[0.0000290121,0.0001234147,0.0005911313,0.000007124601,0.00001931183,0.000002330775,0.0003052528,0.9755173,0.00001749709,0.009220817,0.01255601,0.001610755],"study_design_scores_gemma":[0.0001781093,0.00004481386,0.0003713067,0.0000455149,0.000011999,0.000002391118,0.0001088981,0.97952,0.0001802024,0.01831772,0.001036698,0.0001823502],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006570741,0.0002381703,0.9751542,0.003686576,0.0004285965,0.0009240248,0.0007183123,0.00003438987,0.01224496],"genre_scores_gemma":[0.9165817,0.001834616,0.06805159,0.0000328005,0.000423073,0.0002519163,0.003560164,0.00004141025,0.00922271],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.910011,"threshold_uncertainty_score":0.9999779,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5642433531899352,"score_gpt":0.5464717258657695,"score_spread":0.01777162732416571,"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."}}