{"id":"W4401545477","doi":"10.1007/s41096-024-00204-w","title":"A Uniform Approach for Analyzing Queues with Correlated Interarrival and Service Times","year":2024,"lang":"en","type":"article","venue":"Journal of the Indian Society for Probability and Statistics","topic":"Advanced Queuing Theory Analysis","field":"Business, Management and Accounting","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Bivariate analysis; Queueing theory; Queue; Bulk queue; Computer science; Markov chain; Layered queueing network; Fork–join queue; Mathematics; Applied mathematics; Markov process; Mathematical optimization; Statistics; Queue management system; Computer network","routes":{"ca_aff":true,"ca_fund":false,"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":[0.007854336,0.00158955,0.001970619,0.003439924,0.001288533,0.002581033,0.004178141,0.001267238,0.002437042],"category_scores_gemma":[0.02104235,0.001197208,0.002344701,0.003055245,0.001953967,0.003790197,0.003512241,0.002228451,0.0006369748],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002116255,"about_ca_system_score_gemma":0.003521136,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005171684,"about_ca_topic_score_gemma":0.003989498,"domain_scores_codex":[0.9959055,0.001501248,0.0003080683,0.0007321619,0.001040749,0.000512199],"domain_scores_gemma":[0.9917306,0.003598326,0.0006425522,0.001466364,0.002102779,0.0004594509],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000287454,0.0003515437,0.004889093,0.0002205916,0.0003873471,0.0005151133,0.0002404562,0.4718614,0.01128676,0.4469164,0.002979162,0.06006468],"study_design_scores_gemma":[0.00000879689,0.00004743515,0.0003509356,0.000007581153,0.00003706103,0.00004580888,0.00001671243,0.9751403,0.0006061036,0.0233187,0.000403433,0.00001713102],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005928422,0.0002074484,0.9929188,0.0000362447,0.0000503179,0.00005054712,0.00004230808,0.0001281166,0.0006378549],"genre_scores_gemma":[0.4417375,0.001241918,0.5462589,0.0004208288,0.0006695357,0.0006183005,0.0005210045,0.0003760382,0.008155962],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007854336,"threshold_uncertainty_score":0.04153824,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01271824240385437,"score_gpt":0.2317405693868343,"score_spread":0.2190223269829799,"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."}}