{"id":"W2105055176","doi":"10.1142/s0217595913400022","title":"A SMOOTHING PENALIZED SAMPLE AVERAGE APPROXIMATION METHOD FOR STOCHASTIC PROGRAMS WITH SECOND-ORDER STOCHASTIC DOMINANCE CONSTRAINTS","year":2013,"lang":"en","type":"article","venue":"Asia Pacific Journal of Operational Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"McMaster University","keywords":"Mathematics; Smoothing; Mathematical optimization; Stochastic dominance; Sample size determination; Applied mathematics; Rate of convergence; Bellman equation; Function (biology); Discretization; Minification; Penalty method; Exponential function; Sample (material); Convergence (economics); Computer science; Statistics; Mathematical analysis","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.01211007,0.0001952219,0.0004519905,0.0007182045,0.0005881428,0.001337619,0.0007054667,0.0001110232,0.001754259],"category_scores_gemma":[0.0113306,0.0001280637,0.0001274299,0.001204256,0.0003372193,0.001483614,0.00006991095,0.0005285357,0.00007142544],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001151619,"about_ca_system_score_gemma":0.00118701,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003768493,"about_ca_topic_score_gemma":0.00002042334,"domain_scores_codex":[0.9939592,0.0007393856,0.001179611,0.0004198852,0.003202793,0.0004990957],"domain_scores_gemma":[0.9845381,0.005520951,0.0005830179,0.0003600062,0.008735706,0.0002622243],"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":[0.002519147,0.0009173406,0.00137687,0.00005478684,0.0003707259,0.00003140948,0.009417902,0.4746252,0.004600105,0.04206974,0.02149824,0.4425185],"study_design_scores_gemma":[0.006026294,0.002060505,0.002228006,0.0002653629,0.00004377425,0.0008028119,0.01159845,0.855289,0.0004084746,0.1151943,0.005523952,0.0005590868],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01598252,0.00008869202,0.9789762,0.001964241,0.000150763,0.001563696,0.00002965424,0.000008536847,0.001235662],"genre_scores_gemma":[0.7000574,0.000009042473,0.2982843,0.00002910898,0.0001594389,0.0001754649,0.00003373134,0.00002038401,0.001231152],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6840749,"threshold_uncertainty_score":0.9996991,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1195788968816375,"score_gpt":0.4312643474946223,"score_spread":0.3116854506129847,"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."}}