{"id":"W4391381527","doi":"10.1109/wsc60868.2023.10408364","title":"Efficient Input Uncertainty Quantification for Regenerative Simulation","year":2023,"lang":"en","type":"article","venue":"","topic":"Simulation Techniques and Applications","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Estimator; Computer science; Queueing theory; Confidence interval; Coverage probability; Measure (data warehouse); Algorithm; Statistics; Mathematical optimization; Mathematics; Data mining","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":[],"consensus_categories":[],"category_scores_codex":[0.001772332,0.0000830616,0.0001217189,0.0002815931,0.0002950958,0.0001407717,0.0002674523,0.00006099587,0.0001659755],"category_scores_gemma":[0.001588931,0.0000609804,0.00009115741,0.001614962,0.00003846583,0.00005633743,0.00004122966,0.0000357393,0.0006403405],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004032686,"about_ca_system_score_gemma":0.00003331376,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001146384,"about_ca_topic_score_gemma":0.000009890723,"domain_scores_codex":[0.9983636,0.00005695676,0.0004795098,0.0004108568,0.0005354675,0.0001535685],"domain_scores_gemma":[0.9962471,0.002362158,0.0001543689,0.0005020314,0.0006820268,0.00005232468],"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":[0.000007944122,0.00001633885,0.00009674968,7.232431e-7,0.000001881327,3.282865e-8,0.0001346761,0.8941939,0.001363123,0.07551285,0.009497896,0.01917387],"study_design_scores_gemma":[0.0001294937,0.00001889294,0.001586393,0.000001654753,0.000002746954,7.955675e-8,0.0002237898,0.9129874,0.003206173,0.03892206,0.04284255,0.00007874603],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1360168,0.000004431445,0.8584603,0.002182645,0.0001094506,0.0007748935,0.00003482209,0.0004124466,0.002004271],"genre_scores_gemma":[0.9871213,9.403668e-7,0.004566405,0.0001534117,0.00007199911,0.0002252481,0.00007820119,0.000008944378,0.007773544],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8538939,"threshold_uncertainty_score":0.8230495,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2896051194840555,"score_gpt":0.4968763214465822,"score_spread":0.2072712019625267,"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."}}