{"id":"W2127485070","doi":"10.1080/10556780802079958","title":"The smoothed Monte Carlo method in robustness optimization","year":2008,"lang":"en","type":"article","venue":"Optimization methods & software","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Vancouver Island University","keywords":"Robustness (evolution); Monte Carlo method; Mathematical optimization; Classification of discontinuities; Computer science; Nonlinear system; Monte Carlo integration; Robust optimization; Hybrid Monte Carlo; Algorithm; Mathematics; Markov chain Monte Carlo; Statistics; Physics","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"],"consensus_categories":[],"category_scores_codex":[0.008893084,0.0002969971,0.000485332,0.0003515653,0.0006896232,0.0002232144,0.001014672,0.000222992,0.0002358139],"category_scores_gemma":[0.02335863,0.0001968269,0.0001519291,0.002305554,0.0001827858,0.0004294939,0.000151485,0.0003032825,0.00001183265],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001312324,"about_ca_system_score_gemma":0.0002008121,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003295477,"about_ca_topic_score_gemma":0.000008369197,"domain_scores_codex":[0.9945592,0.002215176,0.001089646,0.0007159464,0.0009532604,0.000466796],"domain_scores_gemma":[0.9897303,0.008017059,0.0003595146,0.001015559,0.0007238888,0.0001536572],"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":[0.00003090048,0.00002978969,0.0005201395,0.000003722791,0.00001044383,0.000008290699,0.0003399069,0.9739764,0.000002922869,0.0002101156,0.0007451137,0.02412223],"study_design_scores_gemma":[0.00041723,0.00002448324,0.0005233664,0.00001701429,0.00001435263,0.00002457218,0.0001451445,0.9960717,0.00003634334,0.0007278969,0.001740454,0.0002574664],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00008193107,0.0006193863,0.9972782,0.0003905,0.0006661892,0.0004450478,0.000006990039,0.0002172001,0.0002945619],"genre_scores_gemma":[0.0009932366,0.0002655431,0.9961724,0.0001012884,0.00007528697,0.00008857934,0.000007059319,0.00004985821,0.002246789],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02386476,"threshold_uncertainty_score":0.984868,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0963803541285717,"score_gpt":0.4017757868587589,"score_spread":0.3053954327301872,"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."}}