{"id":"W1561119721","doi":"10.1109/glocom.1994.513591","title":"A dynamic priority queueing approach to traffic regulation and scheduling in B-ISDN","year":2002,"lang":"en","type":"article","venue":"","topic":"Network Traffic and Congestion Control","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Queueing theory; Computer network; Network packet; Priority ceiling protocol; Scheduling (production processes); Priority inheritance; Integrated Services Digital Network; Multiplexing; Priority queue; Deadline-monotonic scheduling; Statistical time division multiplexing; Real-time computing; Packet switching; Fair queuing; Dynamic priority scheduling; Queue; Round-robin scheduling; Mathematical optimization; Rate-monotonic scheduling; Quality of service; Telecommunications; Mathematics","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":[0.0002300287,0.00009125365,0.0001177337,0.0001121517,0.00006442988,0.0001155213,0.0002022064,0.0000499978,0.000007756334],"category_scores_gemma":[0.00001626609,0.00008759235,0.00001963486,0.0004012493,0.00001405096,0.0002850298,0.00005717457,0.0001025881,0.00001819296],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003713316,"about_ca_system_score_gemma":0.00001009788,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007685201,"about_ca_topic_score_gemma":0.00004699591,"domain_scores_codex":[0.9991154,0.00004588844,0.0001676017,0.0003399177,0.0001289097,0.0002022757],"domain_scores_gemma":[0.9996153,0.00004463512,0.00002688715,0.0002059327,0.00002252363,0.0000847702],"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":[0.000002746779,0.00005851073,0.0001971884,0.000006374252,0.000003182742,0.000001645239,0.0008392477,0.1321021,0.00004631154,0.02816907,0.00003143909,0.8385422],"study_design_scores_gemma":[0.0003203076,0.00001834795,0.004603447,0.00001407444,0.000001575574,0.000007604245,0.00003090122,0.9945775,0.000002054214,0.0001531668,0.0001590512,0.0001119805],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3588077,0.000202906,0.636754,0.001039167,0.00006471176,0.0001815365,7.15868e-8,0.0001578432,0.002792083],"genre_scores_gemma":[0.8874351,0.00000997636,0.111943,0.0002043384,0.00001946434,0.00001333478,3.219168e-7,0.000004017726,0.0003704119],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8624754,"threshold_uncertainty_score":0.3571912,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01253717379610884,"score_gpt":0.2126445598755062,"score_spread":0.2001073860793973,"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."}}