{"id":"W4292056390","doi":"10.1016/j.cie.2022.108551","title":"Multi-stage online robust parameter design based on Bayesian GP model","year":2022,"lang":"en","type":"article","venue":"Computers & Industrial Engineering","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Hyperparameter; Robust optimization; Mathematical optimization; Robustness (evolution); Cluster analysis; Data mining; Machine learning; Mathematics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.003230545,0.0003954741,0.0005310221,0.0007012313,0.0002687879,0.0002808943,0.001426889,0.0001586661,0.0003122652],"category_scores_gemma":[0.001535307,0.0003832933,0.0002470898,0.001176979,0.00004540759,0.0002359878,0.0004688642,0.0009738525,0.00002122925],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003835856,"about_ca_system_score_gemma":0.0001776431,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001402769,"about_ca_topic_score_gemma":2.576506e-7,"domain_scores_codex":[0.9955514,0.0006715888,0.0008046983,0.000878837,0.001529379,0.0005640849],"domain_scores_gemma":[0.9955565,0.003015161,0.0002035295,0.0008683886,0.00006386987,0.0002925002],"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":[0.0001441851,0.0002023947,0.00002417523,0.000001458889,0.00001598871,0.0000409262,0.0001059298,0.9822892,0.00426531,0.00007136063,0.001624414,0.01121465],"study_design_scores_gemma":[0.001823894,0.0003807264,0.0000359658,0.00001936225,0.000009628866,0.000004842839,0.00009056288,0.9937239,0.001663873,0.00002811016,0.001809867,0.0004092199],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007552274,0.000026485,0.9891616,0.000194746,0.00213705,0.0005754571,0.00007720502,0.0002258942,0.0000492525],"genre_scores_gemma":[0.2253957,3.428098e-7,0.7734002,0.0004787623,0.000220121,0.00006193291,0.00001730805,0.00006229422,0.000363366],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2178434,"threshold_uncertainty_score":0.9998619,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4358465366583615,"score_gpt":0.4000761011655973,"score_spread":0.03577043549276426,"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."}}