{"id":"W1975712057","doi":"10.1016/j.apm.2012.09.046","title":"A note on the distributional Little’s law for discrete-time queues with D-MAP arrivals and its application","year":2012,"lang":"en","type":"article","venue":"Applied Mathematical Modelling","topic":"Advanced Queuing Theory Analysis","field":"Business, Management and Accounting","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Royal Military College of Canada","funders":"Natural Sciences and Engineering Research Council of Canada; University of North Carolina at Charlotte","keywords":"FIFO (computing and electronics); Markovian arrival process; Queue; Class (philosophy); Computer science; Relation (database); Queueing theory; Bulk queue; Sample (material); Distribution (mathematics); Real-time computing; Mathematics; Mathematical optimization; Applied mathematics; Computer network; Mathematical analysis; Physics; Data mining; Artificial intelligence","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":[0.006117989,0.001390543,0.001871072,0.002910989,0.001490759,0.00353469,0.002838834,0.003617957,0.003973098],"category_scores_gemma":[0.02374225,0.0008573253,0.002802849,0.003185965,0.005839009,0.007724598,0.003892906,0.007391034,0.0009907524],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002049379,"about_ca_system_score_gemma":0.001683239,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0024779,"about_ca_topic_score_gemma":0.0011604,"domain_scores_codex":[0.998206,0.0006381439,0.0001434111,0.0003285193,0.0005731205,0.0001108904],"domain_scores_gemma":[0.990418,0.006578503,0.0003765591,0.0007327863,0.001352122,0.0005420801],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00003041191,0.00003204923,0.0002097553,0.00009316397,0.0000217132,0.0002545719,0.0001619665,0.006491832,0.0009816227,0.9802623,0.003595969,0.00786464],"study_design_scores_gemma":[0.000008345787,0.00003392283,0.0001732211,0.00003338932,0.00001411974,0.0002011072,0.00003832247,0.08894867,0.0003825129,0.9004285,0.009698291,0.00003957604],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01281841,0.01113413,0.9289098,0.009556451,0.005471848,0.00005021501,0.0001605099,0.0002351272,0.03166356],"genre_scores_gemma":[0.5767365,0.02960666,0.315558,0.009626498,0.01901895,0.0002779042,0.0004181325,0.0005871546,0.04817007],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006117989,"threshold_uncertainty_score":0.03235537,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01958013515916219,"score_gpt":0.2427185086852626,"score_spread":0.2231383735261004,"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."}}