{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01092845,0.0008278847,0.001147143,0.00170215,0.0004456171,0.001676958,0.002113645,0.001115911,0.001343927],"category_scores_gemma":[0.05706637,0.0005058759,0.0007782917,0.001038144,0.001748013,0.002809221,0.00278801,0.001656185,0.000192988],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001810113,"about_ca_system_score_gemma":0.001253473,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001569318,"about_ca_topic_score_gemma":0.0008109396,"domain_scores_codex":[0.9935918,0.003358349,0.0002680846,0.0005336785,0.001958025,0.0002900801],"domain_scores_gemma":[0.9649456,0.028114,0.002008615,0.002521353,0.002124779,0.000285667],"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":[0.000130864,0.0000537571,0.001767403,0.0001617373,0.00005231807,0.00008740672,0.0001523726,0.7908456,0.003265961,0.1549951,0.00041709,0.04807047],"study_design_scores_gemma":[0.000003305108,0.00001724666,0.0001218155,0.00001420823,0.000005329804,0.00001707731,0.000009207178,0.9668962,0.001304367,0.03137852,0.0002240823,0.00000867128],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007873798,0.0001085174,0.9911972,0.0000515438,0.000006287523,0.00001861832,0.00002230903,0.0001597143,0.000561917],"genre_scores_gemma":[0.7436375,0.0002732827,0.2549797,0.00008361418,0.00003995468,0.0001664353,0.0001630079,0.0001361509,0.000520408],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01092845,"threshold_uncertainty_score":0.05779588,"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."}}