{"id":"W2154813964","doi":"10.1109/glocom.2002.1189090","title":"Effective bandwidth of multiclass Markovian traffic sources and admission control with dynamic buffer partitioning","year":2003,"lang":"en","type":"article","venue":"","topic":"Network Traffic and Congestion Control","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Markov process; Computer science; Quality of service; Buffer overflow; Bandwidth (computing); Admission control; Multiplexing; Statistical time division multiplexing; Packet loss; Markov chain; Computer network; Network packet; Mathematics; Telecommunications; Statistics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002587,0.0001417728,0.0002179304,0.00005792492,0.0001158051,0.00006644589,0.000138131,0.00005120921,0.00003123196],"category_scores_gemma":[0.00003803382,0.00009962539,0.00003639763,0.0001676364,0.00008013567,0.0002011418,0.00001505626,0.00009368614,0.000003580336],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001697417,"about_ca_system_score_gemma":0.00004423768,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004489148,"about_ca_topic_score_gemma":0.00003354953,"domain_scores_codex":[0.9989567,0.0001803314,0.000169698,0.0003029936,0.0001697101,0.0002205059],"domain_scores_gemma":[0.9991748,0.0003334658,0.00008286799,0.0001981966,0.00007518318,0.0001354376],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002881426,0.0002778224,0.01093425,0.00006246405,0.0001998648,0.000026543,0.001088549,0.02648964,0.0009591114,0.03729961,0.000152224,0.9222218],"study_design_scores_gemma":[0.004001352,0.0004602709,0.0126669,0.00008553245,0.00004001781,0.00003433372,0.00009834149,0.9810627,0.0004834206,0.0001586115,0.0006729471,0.0002355683],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4602572,0.0002642954,0.5377876,0.0003265963,0.00006444725,0.0003431,8.745101e-7,0.000109836,0.0008461309],"genre_scores_gemma":[0.9925524,0.000009091082,0.006981312,0.0001529694,0.00001050844,0.00003488502,5.668479e-7,0.000007557794,0.000250731],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.954573,"threshold_uncertainty_score":0.4062606,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.002261017808771911,"score_gpt":0.1851888633673635,"score_spread":0.1829278455585915,"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."}}