{"id":"W2059826035","doi":"10.1109/mwc.2003.1209591","title":"Soft QoS provisioning using the token bank fair queuing scheduling algorithm","year":2003,"lang":"en","type":"article","venue":"IEEE Wireless Communications","topic":"Advanced Wireless Network Optimization","field":"Engineering","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Communications Research Centre Canada","funders":"","keywords":"Computer science; Computer network; Fair queuing; Token bucket; Quality of service; Network packet; Scheduling (production processes); Provisioning; Statistical time division multiplexing; Round-robin scheduling; Distributed computing; Wireless network; Algorithm; Wireless; Dynamic priority scheduling; Multiplexing; Telecommunications","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003410291,0.0002638777,0.0002373406,0.000109542,0.0009807111,0.0001308724,0.0009459263,0.0001288685,0.00001639289],"category_scores_gemma":[0.0000599778,0.0002490646,0.0000847753,0.0006823226,0.0001627139,0.0004623636,0.0001182548,0.0006129034,0.00001854018],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002078068,"about_ca_system_score_gemma":0.00006828877,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001875237,"about_ca_topic_score_gemma":0.00002829508,"domain_scores_codex":[0.9984536,0.0002101307,0.0004592632,0.0002267757,0.0002181983,0.0004319954],"domain_scores_gemma":[0.9974729,0.0003909901,0.0001237608,0.00177967,0.000145959,0.0000867193],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[8.055953e-7,0.00002376611,0.00008302178,0.00001371246,0.00003512666,7.347416e-7,0.0004816477,0.9672179,0.003408495,0.001953084,0.00004229179,0.02673942],"study_design_scores_gemma":[0.0002024552,0.000005498297,0.00002206849,0.0001390648,0.00002953485,0.00001655243,0.0005055523,0.9928694,0.002723496,0.0002188621,0.002969645,0.0002978815],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02233737,0.001851969,0.9726687,0.00009403922,0.0005538932,0.0004278148,0.000007330828,0.0006152061,0.001443664],"genre_scores_gemma":[0.7127191,0.0005851251,0.286313,0.00005015831,0.0001081522,0.00008333463,0.00002068527,0.00008851521,0.00003192154],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6903818,"threshold_uncertainty_score":0.9999962,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02519009916673648,"score_gpt":0.2676101587157301,"score_spread":0.2424200595489936,"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."}}