{"id":"W1976181481","doi":"10.1016/s0140-3664(00)00185-7","title":"Design and analysis of per-flow queueing switches and VC-merge switches based on per-VC queueing architecture","year":2000,"lang":"en","type":"article","venue":"Computer Communications","topic":"Network Traffic and Congestion Control","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Queueing theory; Merge (version control); Layered queueing network; Weighted fair queueing; Mean value analysis; Queueing system; Architecture; Computer network; Parallel computing","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004229783,0.0002287324,0.0004019492,0.0003486027,0.0004163569,0.0001889683,0.001326268,0.00008521802,0.00003488945],"category_scores_gemma":[0.0000108102,0.0002156955,0.0001219894,0.0007126387,0.0001802646,0.0001853213,0.000305894,0.0002954216,0.000006649084],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002314907,"about_ca_system_score_gemma":0.00007051915,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004743571,"about_ca_topic_score_gemma":0.00005568461,"domain_scores_codex":[0.9984012,0.0003416776,0.0003697888,0.0004348754,0.0002032912,0.0002491872],"domain_scores_gemma":[0.9967218,0.001050438,0.0001284513,0.00187312,0.00009343863,0.0001327577],"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":[0.00001738765,0.0001171439,0.001198804,0.000010738,0.0002284785,9.773476e-7,0.001622236,0.2453306,0.0000966352,0.002435083,0.00009216736,0.7488497],"study_design_scores_gemma":[0.0003576608,0.00009256653,0.008206945,0.0000634221,0.0001741502,0.000004646241,0.00001432647,0.9889688,0.00002318911,0.0002982714,0.001579548,0.0002165121],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02153649,0.001401193,0.9718916,0.004300147,0.00004306298,0.0002311138,0.000004055094,0.0001645375,0.0004277795],"genre_scores_gemma":[0.6988758,0.0002225731,0.3002425,0.0005467607,0.0000280566,0.00002503715,0.00001090081,0.00001002817,0.00003831237],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7486332,"threshold_uncertainty_score":0.8795809,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01895260254583411,"score_gpt":0.2349403952438114,"score_spread":0.2159877926979773,"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."}}