{"id":"W2100876404","doi":"10.1109/glocom.2010.5684172","title":"A Model for Steady State Throughput of TCP CUBIC","year":2010,"lang":"en","type":"article","venue":"","topic":"Network Traffic and Congestion Control","field":"Computer Science","cited_by":48,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Vedecká Grantová Agentúra MŠVVaŠ SR a SAV","keywords":"CUBIC TCP; Throughput; Computer science; TCP acceleration; TCP global synchronization; Transmission Control Protocol; TCP Friendly Rate Control; Packet loss; Algorithm; Computer network; Network packet; Wireless; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001388693,0.0009336033,0.001026038,0.001299183,0.0009681972,0.001546044,0.002696937,0.001513264,0.003200704],"category_scores_gemma":[0.00553097,0.0005871217,0.001046614,0.00141402,0.001201253,0.002694552,0.0007777935,0.002013915,0.0007661873],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002731076,"about_ca_system_score_gemma":0.001982102,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009640524,"about_ca_topic_score_gemma":0.003769885,"domain_scores_codex":[0.9987495,0.0002309745,0.00006031716,0.0003162911,0.0003534794,0.0002893816],"domain_scores_gemma":[0.997306,0.001275476,0.0003227301,0.0002521619,0.0007544971,0.00008910774],"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":[0.0001602094,0.0001043841,0.001388483,0.0001179245,0.00004083211,0.0002341621,0.0003214082,0.8843161,0.005809234,0.09638482,0.002532477,0.008590044],"study_design_scores_gemma":[0.000007395417,0.00002160024,0.0001267062,0.000006333693,0.000009996213,0.00003865192,0.00001105489,0.9917842,0.0003847622,0.00719704,0.0004023285,0.000009822202],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07902058,0.0006874747,0.9045312,0.0008682438,0.0001363395,0.0002182178,0.0008289317,0.001572784,0.01213636],"genre_scores_gemma":[0.9595684,0.0007540994,0.0317855,0.0001543079,0.0001007923,0.0006089096,0.0005075096,0.0001519074,0.006368594],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009640524,"threshold_uncertainty_score":0.01981539,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02124505172614448,"score_gpt":0.2517912544371289,"score_spread":0.2305462027109844,"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."}}