{"id":"W2606159080","doi":"10.1109/blackseacom.2016.7901568","title":"Prediction and preemptive control of network congestion in distributed real-time environment","year":2016,"lang":"en","type":"article","venue":"","topic":"Network Traffic and Congestion Control","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Northern British Columbia","funders":"","keywords":"Network congestion; Computer science; Bandwidth throttling; Computer network; Network traffic control; Explicit Congestion Notification; Latency (audio); Packet loss; Throughput; Quality of service; Real-time computing; Distributed computing; Network packet; Slow-start; Engineering; Operating system; Wireless","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":[0.001006645,0.0004181311,0.0003951747,0.0002728733,0.0003992628,0.0005465432,0.001084922,0.0003698496,0.0004459363],"category_scores_gemma":[0.00298664,0.0001553303,0.0001243091,0.0002061829,0.0004585736,0.0009112873,0.0004068274,0.000517786,0.00007443937],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004886432,"about_ca_system_score_gemma":0.0006435212,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00248846,"about_ca_topic_score_gemma":0.002670299,"domain_scores_codex":[0.9995378,0.0001376548,0.0000278343,0.000106053,0.0001374195,0.00005316545],"domain_scores_gemma":[0.9977992,0.00119776,0.0003337754,0.0002511624,0.000319732,0.00009832087],"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":[0.0005268435,0.0002870086,0.00540141,0.00008838383,0.00004608541,0.0002073819,0.0001281698,0.8166752,0.04938006,0.003491927,0.0009077786,0.1228598],"study_design_scores_gemma":[0.000008278816,0.00005985355,0.0003507868,0.000001589261,0.000004960702,0.00002066967,0.00001160756,0.9937769,0.004944372,0.0007212608,0.00009528082,0.000004489864],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3743851,0.0004105758,0.62135,0.0002385353,0.00007780478,0.00007296428,0.00004219221,0.00168607,0.001736618],"genre_scores_gemma":[0.9827589,0.00005824913,0.01675591,0.00001714567,0.00000974902,0.00001632451,0.00001601246,0.00001292345,0.0003548114],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00248846,"threshold_uncertainty_score":0.005323708,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005650794717790208,"score_gpt":0.1722687529468392,"score_spread":0.166617958229049,"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."}}