{"id":"W3217601300","doi":"10.1007/978-3-030-91825-5_24","title":"A Short Note on the System-Length Distribution in a Finite-Buffer $$GI^X/C$$-MSP/1/N Queue Using Roots","year":2021,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Queuing Theory Analysis","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Royal Military College of Canada","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Markovian arrival process; Computer science; Queue; Markov chain; Queueing theory; Markov process; Stationary distribution; Buffer (optical fiber); Distribution (mathematics); Queueing system; Exponential distribution; Mathematical optimization; Algorithm; Applied mathematics; Mathematics; Computer network; Statistics; Mathematical analysis; Telecommunications","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":[0.001631185,0.001354278,0.001184835,0.0009799574,0.0009439915,0.001890391,0.002433401,0.001099988,0.00831057],"category_scores_gemma":[0.005789867,0.0007251605,0.0009799266,0.001907807,0.001967616,0.004443804,0.001417428,0.004037341,0.002159612],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002229191,"about_ca_system_score_gemma":0.001031465,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003105908,"about_ca_topic_score_gemma":0.002640157,"domain_scores_codex":[0.9994144,0.0001218738,0.00002933499,0.0001519985,0.0002340064,0.00004844179],"domain_scores_gemma":[0.9984376,0.00114632,0.00006156999,0.000111728,0.0001766864,0.000066219],"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":[0.00007910337,0.00004678021,0.0002726773,0.0002264944,0.00002797138,0.0003720325,0.0002557424,0.03081331,0.004789131,0.9026139,0.02119238,0.03931032],"study_design_scores_gemma":[0.0000156525,0.00005291286,0.0004610037,0.00007193122,0.00002418423,0.0003693202,0.00005256055,0.2499449,0.001764162,0.7108887,0.03629412,0.00006051431],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006434006,0.007149086,0.9507599,0.001807224,0.003139601,0.00005446233,0.0002402702,0.0009962085,0.02941924],"genre_scores_gemma":[0.3981639,0.01955298,0.4387456,0.002483437,0.008625874,0.0002479867,0.0005758219,0.00297753,0.1286269],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00831057,"threshold_uncertainty_score":0.02780157,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02029324971301419,"score_gpt":0.2451065553477358,"score_spread":0.2248133056347217,"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."}}