{"id":"W4232051886","doi":"10.32920/ryerson.14654205","title":"Admission Control and Bandwidth Allocation for Class A Traffic in RPR Networks","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Network Traffic and Congestion Control","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Dynamic bandwidth allocation; Computer science; Bandwidth allocation; Admission control; Bandwidth (computing); Network traffic control; Quality of service; Computer network; Traffic shaping; Bandwidth management; Traffic policing; Internet traffic; Channel allocation schemes; The Internet; Distributed computing; Real-time computing; Network packet; 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":[0.00224177,0.0006158442,0.0006366416,0.0009145836,0.0008107491,0.001858834,0.0016382,0.00123951,0.001008391],"category_scores_gemma":[0.006590946,0.0003298913,0.0005795361,0.0008409939,0.001313453,0.00268297,0.0007785117,0.0014183,0.0002868905],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001403877,"about_ca_system_score_gemma":0.001272074,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002291386,"about_ca_topic_score_gemma":0.001023197,"domain_scores_codex":[0.9974561,0.0008547668,0.0001284055,0.0003145762,0.001028598,0.0002174302],"domain_scores_gemma":[0.9973929,0.001487676,0.0003940535,0.0002695666,0.0004008986,0.00005491698],"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":[0.0001670902,0.0002991977,0.001804022,0.000198773,0.00007209345,0.0002789827,0.000495589,0.4885732,0.03266354,0.2791901,0.002096473,0.1941609],"study_design_scores_gemma":[0.00001094146,0.00005300605,0.0002602873,0.00001421472,0.00001162078,0.0001061869,0.00003641326,0.9801887,0.004002399,0.01347572,0.001819587,0.0000208571],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0465192,0.001116313,0.9442795,0.0003293598,0.0001099831,0.0001125859,0.00001744021,0.000322845,0.007192757],"genre_scores_gemma":[0.8114299,0.001761438,0.1803447,0.0001880269,0.0002835077,0.0002705831,0.00006380153,0.00009500309,0.005563057],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002291386,"threshold_uncertainty_score":0.01185578,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0103711103953047,"score_gpt":0.2331299028650147,"score_spread":0.22275879246971,"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."}}