{"id":"W2013001756","doi":"10.1287/ijoc.1050.0157","title":"A Survey and Experimental Comparison of Service-Level-Approximation Methods for Nonstationary<i>M(t)/M/s(t)</i>Queueing Systems with Exhaustive Discipline","year":2007,"lang":"en","type":"article","venue":"INFORMS journal on computing","topic":"Advanced Queuing Theory Analysis","field":"Business, Management and Accounting","cited_by":115,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Queueing theory; Approximation algorithm; Computation; Approximation error; Computer science; Ordinary differential equation; Mathematical optimization; Solver; Set (abstract data type); Closure (psychology); Applied mathematics; Mathematics; Differential equation; Algorithm; Statistics; Mathematical analysis","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":[0.004014133,0.0001999309,0.0004118198,0.0004062087,0.0004469664,0.0002266433,0.0001615867,0.00005036488,0.000003850105],"category_scores_gemma":[0.0001982265,0.0001498353,0.00005788638,0.0005700402,0.0000510904,0.001128446,0.00010178,0.0002076458,0.000001719686],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007482003,"about_ca_system_score_gemma":0.00001822284,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007174438,"about_ca_topic_score_gemma":0.00004779696,"domain_scores_codex":[0.9984639,0.00002800283,0.0008040277,0.0001726084,0.0002612513,0.0002702682],"domain_scores_gemma":[0.9971085,0.0007633237,0.001346294,0.0001127168,0.0006398239,0.00002932509],"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":[0.003203476,0.000395712,0.0754667,0.0008946997,0.0005091723,0.000009743857,0.004011576,0.7835652,0.002381523,0.02075147,0.00008754689,0.1087232],"study_design_scores_gemma":[0.001565189,0.0001385769,0.01284132,0.0005234529,0.00007173205,0.00002691297,0.007163289,0.9747036,0.001529061,0.0008297129,0.0002803093,0.0003268858],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4147168,0.00008612512,0.5845451,0.00004782023,0.0001327946,0.0002116587,0.00000211721,0.0000243317,0.0002332992],"genre_scores_gemma":[0.9357587,5.528081e-7,0.06357275,0.0002718768,0.0003268674,0.000003127784,0.00003558267,0.00002269079,0.00000787016],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5210419,"threshold_uncertainty_score":0.6110108,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05855104518789757,"score_gpt":0.3763409439001331,"score_spread":0.3177898987122356,"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."}}