{"id":"W2103375672","doi":"10.1109/glocom.2006.320","title":"NXG01-3: Rate Allocation under Network End-to-End Quality-of-Service Requirements","year":2006,"lang":"en","type":"article","venue":"Globecom","topic":"Network Traffic and Congestion Control","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Quality of service; End-to-end principle; Computer network; Probabilistic logic; End-to-end delay; Bandwidth (computing); Bandwidth allocation; Queue; Network delay; Queuing delay; Mathematical optimization; Network packet; Distributed computing","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":[0.0008002025,0.0001659656,0.0002373245,0.00005468799,0.0001304076,0.0001006082,0.0007442621,0.00007261612,0.00007509653],"category_scores_gemma":[0.00001413815,0.0001680481,0.00006759181,0.0007343603,0.00002447613,0.00028768,0.0001668428,0.00009331279,0.0002269804],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006202226,"about_ca_system_score_gemma":0.00008981941,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008823853,"about_ca_topic_score_gemma":0.0015155,"domain_scores_codex":[0.9981482,0.0002170212,0.0005045471,0.0004030897,0.0003341143,0.0003930314],"domain_scores_gemma":[0.9987217,0.0001615658,0.0002060419,0.0006166661,0.000193279,0.0001007788],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004843845,0.0001646407,0.002664327,0.00002606213,0.00006239628,0.000003965916,0.0001217936,0.1324158,0.000868635,0.6281521,0.01681931,0.2186525],"study_design_scores_gemma":[0.005678602,0.0004703114,0.3618687,0.0003074088,0.0001122277,0.000013736,0.0001270012,0.3679453,0.001004663,0.1215894,0.1387569,0.002125705],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05855669,0.0003257612,0.920802,0.01169142,0.001272853,0.0003588202,0.000005415412,0.000281558,0.006705497],"genre_scores_gemma":[0.9835687,0.000004838355,0.01035202,0.004967943,0.0004033849,0.00002446643,0.00001950184,0.00001023832,0.0006489257],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.925012,"threshold_uncertainty_score":0.6852803,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03039839090807506,"score_gpt":0.2793073512016679,"score_spread":0.2489089602935929,"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."}}