{"id":"W2006890568","doi":"10.1002/nem.407","title":"Weighted proportional window control of TCP traffic","year":2001,"lang":"en","type":"article","venue":"International Journal of Network Management","topic":"Network Traffic and Congestion Control","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Nortel (Canada)","funders":"","keywords":"Bottleneck; Computer science; Window (computing); TCP global synchronization; TCP Friendly Rate Control; Computer network; TCP acceleration; H-TCP; TCP tuning; TCP Westwood plus; Connection (principal bundle); Service (business); Control (management); Network congestion; Operating system; Mathematics; Artificial intelligence; Embedded system","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":[0.001329416,0.0006044921,0.0004413884,0.0007241078,0.0004549293,0.001387494,0.00157765,0.0004170404,0.001900251],"category_scores_gemma":[0.006061701,0.0002151034,0.0002609277,0.0006625979,0.0007665086,0.001417209,0.001021853,0.0008792103,0.0002916113],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005649477,"about_ca_system_score_gemma":0.0004733065,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001383796,"about_ca_topic_score_gemma":0.0009829154,"domain_scores_codex":[0.9983883,0.0003217002,0.00007529591,0.0002554672,0.0008058093,0.0001533487],"domain_scores_gemma":[0.998187,0.0007419073,0.0001544248,0.0003030944,0.0005256803,0.00008786013],"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":[0.0009574332,0.0003508143,0.00179237,0.0002694906,0.0001375474,0.0003546539,0.0003502123,0.2097761,0.1544196,0.06960212,0.003791291,0.5581983],"study_design_scores_gemma":[0.00004777453,0.0001373854,0.0003670408,0.00001547696,0.00004873701,0.000146589,0.00001975899,0.9492958,0.02931455,0.01505064,0.005529963,0.00002625441],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05848697,0.00105013,0.9326791,0.000117378,0.0003201234,0.0001110952,0.00003461297,0.001430059,0.005770574],"genre_scores_gemma":[0.9405339,0.0004078948,0.05545912,0.00007567375,0.0002054181,0.0000704117,0.0000352571,0.0000855347,0.003126691],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001900251,"threshold_uncertainty_score":0.007030725,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006698769925220392,"score_gpt":0.2251136874426944,"score_spread":0.218414917517474,"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."}}