{"id":"W2000297053","doi":"10.1109/noms.2006.1687666","title":"A Bandwidth Bargain Model based on Adaptive Weighted Fair Queueing","year":2006,"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 Guelph","funders":"","keywords":"Weighted fair queueing; Computer science; Computer network; Bandwidth (computing); Queueing theory; Quality of service; Bandwidth allocation; Dynamic bandwidth allocation; Network packet; Proportionally fair; Fair queuing; Distributed computing; Dynamic priority scheduling; Round-robin scheduling","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":[0.001744404,0.0007534476,0.001186908,0.0005669522,0.0008027576,0.00168312,0.003107336,0.001410248,0.00319147],"category_scores_gemma":[0.003274652,0.0003802934,0.0006895803,0.0008657942,0.001617206,0.002742276,0.001130654,0.001485541,0.0004494088],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001680182,"about_ca_system_score_gemma":0.001472471,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00817924,"about_ca_topic_score_gemma":0.003895798,"domain_scores_codex":[0.9985983,0.0004138916,0.00004776663,0.0002440352,0.0004716551,0.0002244719],"domain_scores_gemma":[0.9992816,0.000326478,0.00008510006,0.00006911596,0.000170429,0.00006732823],"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":[0.00007473604,0.00006263349,0.0002429382,0.00003976901,0.00003003296,0.0001860339,0.0001431653,0.7667418,0.002579514,0.2182851,0.001023046,0.01059108],"study_design_scores_gemma":[0.000009440325,0.00001452549,0.0000200841,0.000001279505,0.00000443161,0.00001205798,0.000006697584,0.9832554,0.0001326283,0.01614835,0.0003895161,0.000005585803],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02033944,0.0002057566,0.9716689,0.0002867065,0.0001002765,0.00007900952,0.0000625915,0.0002186549,0.007038742],"genre_scores_gemma":[0.9170722,0.0004097777,0.07093521,0.0001377807,0.00009924976,0.0002407827,0.00007845172,0.00005374888,0.01097282],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00817924,"threshold_uncertainty_score":0.01626331,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007061470665946677,"score_gpt":0.1901878412449711,"score_spread":0.1831263705790245,"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."}}