{"id":"W2079943270","doi":"10.1007/s11276-006-7527-9","title":"End-to-end delay margin balancing approach for routing in multi-class networks","year":2006,"lang":"en","type":"article","venue":"Wireless Networks","topic":"Network Traffic and Congestion Control","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Static routing; Routing (electronic design automation); Heuristic; Equal-cost multi-path routing; Mathematical optimization; Elmore delay; Computer network; Routing protocol; Mathematics; Propagation delay","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":[0.001088489,0.0005849523,0.0006433649,0.0004910642,0.0007413671,0.0009234098,0.001669582,0.0005717422,0.002388702],"category_scores_gemma":[0.001562458,0.0001872673,0.0002500952,0.000451768,0.0003819138,0.001425327,0.0007855724,0.0008963515,0.0002709422],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001003045,"about_ca_system_score_gemma":0.0005474738,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001068699,"about_ca_topic_score_gemma":0.00291069,"domain_scores_codex":[0.9995456,0.0001218322,0.00001939589,0.0000758207,0.0001696217,0.00006759],"domain_scores_gemma":[0.9993838,0.0002503108,0.00005434223,0.00008090052,0.0001857986,0.00004476541],"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":[0.0006010157,0.0002935659,0.0008649832,0.0001352219,0.00007100397,0.0001064042,0.0001955995,0.6548853,0.0254223,0.04332779,0.003990249,0.2701066],"study_design_scores_gemma":[0.00000936103,0.00003320831,0.0001155734,0.000002778213,0.00001031308,0.00002155791,0.00001445247,0.9918268,0.001812737,0.005573579,0.0005749708,0.000004681517],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02338045,0.000267244,0.974006,0.0002020388,0.00007503771,0.0000357827,0.00003058025,0.0001990564,0.001803861],"genre_scores_gemma":[0.8442818,0.000258629,0.1501743,0.0001452617,0.0001746893,0.00007000729,0.00006289504,0.00009061587,0.004741871],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002388702,"threshold_uncertainty_score":0.007991076,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01166750831634744,"score_gpt":0.2203023191741937,"score_spread":0.2086348108578462,"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."}}