{"id":"W2101113883","doi":"10.1109/jlt.2007.912057","title":"A Novel Congestion Detection Scheme in TCP Over OBS Networks","year":2009,"lang":"en","type":"article","venue":"Journal of Lightwave Technology","topic":"Network Traffic and Congestion Control","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; CUBIC TCP; Computer network; TCP Westwood plus; TCP Friendly Rate Control; Network congestion; Packet loss; TCP tuning; Throughput; H-TCP; Overhead (engineering); Network packet; Bandwidth (computing); TCP global synchronization; Real-time computing; 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":[],"consensus_categories":[],"category_scores_codex":[0.0004133058,0.0001414554,0.0002993871,0.0006690176,0.00005202124,0.00005017599,0.0005860873,0.000312958,0.000007397031],"category_scores_gemma":[0.00009044386,0.0001247621,0.00008619364,0.0009802463,0.00004849234,0.0003858545,0.00004767303,0.0006629315,0.000005757383],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001089906,"about_ca_system_score_gemma":0.00006241894,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001686411,"about_ca_topic_score_gemma":0.00002000049,"domain_scores_codex":[0.998722,0.00003551017,0.0005407289,0.0002032213,0.0002068522,0.0002917484],"domain_scores_gemma":[0.9989818,0.00006693621,0.000421214,0.0002726454,0.0001886976,0.00006864979],"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":[0.00006567641,0.0001991683,0.0008827209,0.00000179081,0.00002743451,0.0001242839,0.00004092744,0.002298075,0.02011126,0.07003933,0.0003027205,0.9059066],"study_design_scores_gemma":[0.00772275,0.003095681,0.06559914,0.0003391267,0.00005047293,0.002788279,0.00005648481,0.8620296,0.007436655,0.03816228,0.01193391,0.0007856725],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1117688,0.0007995678,0.8784214,0.007792446,0.0007043796,0.0001041162,1.451819e-7,0.0001498488,0.0002592781],"genre_scores_gemma":[0.9833944,0.00008567904,0.01569012,0.0005632876,0.0002164735,0.000002675961,1.302175e-7,0.0000054007,0.00004176146],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.905121,"threshold_uncertainty_score":0.5087653,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006569153266655478,"score_gpt":0.2219157603206292,"score_spread":0.2153466070539737,"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."}}