{"id":"W2171072529","doi":"10.1177/0037549713508334","title":"Factors affecting warm-up periods in discrete event simulation","year":2013,"lang":"en","type":"article","venue":"SIMULATION","topic":"Simulation Techniques and Applications","field":"Decision Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Initialization; Mean squared error; Event (particle physics); Mathematics; Monte Carlo method; Statistics; Queueing theory; Computer science; Applied mathematics; Variable (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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03151313,0.0008232057,0.0013725,0.0008736648,0.001452953,0.001786228,0.001492154,0.001245843,0.001327383],"category_scores_gemma":[0.1866755,0.0007741411,0.0008122436,0.0008831405,0.001605406,0.003786333,0.001693806,0.002753094,0.000255642],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002043138,"about_ca_system_score_gemma":0.001929299,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002123023,"about_ca_topic_score_gemma":0.002002431,"domain_scores_codex":[0.9837736,0.01060309,0.001156546,0.001467732,0.001928629,0.001070406],"domain_scores_gemma":[0.7609845,0.205134,0.01198194,0.01420653,0.005850466,0.00184248],"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":[0.001905299,0.0003907754,0.04678437,0.0004524505,0.0003306709,0.000509655,0.001051976,0.8380612,0.01254726,0.05193474,0.001761893,0.04426965],"study_design_scores_gemma":[0.0001984787,0.0006281155,0.009795829,0.0001736072,0.0001834077,0.0002855175,0.0002500492,0.9313571,0.01967702,0.03496237,0.002347733,0.0001408565],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.378635,0.00205084,0.612938,0.001537431,0.0002989924,0.0003053478,0.0001521677,0.001140182,0.002941974],"genre_scores_gemma":[0.9582673,0.0002615015,0.04040718,0.0003100004,0.00005974386,0.0001676594,0.0001084406,0.0001392864,0.0002788231],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03151313,"threshold_uncertainty_score":0.1666593,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1620184527003988,"score_gpt":0.4706852088219431,"score_spread":0.3086667561215443,"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."}}