{"id":"W2770845104","doi":"10.1002/net.21792","title":"Open shortest path first routing under random early detection","year":2017,"lang":"en","type":"article","venue":"Networks","topic":"Network Traffic and Congestion Control","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Open Shortest Path First; Path vector protocol; Shortest path problem; Equal-cost multi-path routing; Static routing; Private Network-to-Network Interface; Routing (electronic design automation); Constrained Shortest Path First; Link-state routing protocol; Computer network; Routing Information Protocol; Routing protocol; Mathematical optimization; Distributed computing; K shortest path routing; Mathematics; Theoretical computer science; Graph","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":["sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0005527363,0.0001587985,0.0002184958,0.0000236128,0.001412619,0.002307851,0.002449667,0.0001237086,0.00001759593],"category_scores_gemma":[0.00004161443,0.0001459692,0.00007611388,0.00009324776,0.00005438736,0.0009785054,0.0007154838,0.0002682766,0.00004447479],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003606352,"about_ca_system_score_gemma":0.00003014827,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001843581,"about_ca_topic_score_gemma":0.0005464315,"domain_scores_codex":[0.9987112,0.0000641513,0.0002215579,0.0004409818,0.0001764286,0.0003856804],"domain_scores_gemma":[0.998395,0.0001666503,0.0002119176,0.001039299,0.0000621135,0.0001250639],"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":[0.00006199195,0.00003566,0.00444249,0.000001133171,0.00003970199,0.00001879219,0.0001084017,0.06702939,0.000005255578,0.01254312,0.001355888,0.9143582],"study_design_scores_gemma":[0.001725121,0.0000581698,0.03200101,0.00004566734,0.00001355549,0.000008997582,0.000006007004,0.962131,0.000006048292,0.0006671427,0.003132229,0.0002050886],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007899494,0.0001374723,0.9818631,0.001289465,0.001507904,0.000345539,3.502834e-7,0.0001872512,0.006769357],"genre_scores_gemma":[0.9971926,0.00003242088,0.0009627371,0.0004463825,0.0007664589,0.00004847792,0.000001073352,0.0000137586,0.0005360736],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9892931,"threshold_uncertainty_score":0.9998874,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01773501321753399,"score_gpt":0.2434218149528389,"score_spread":0.2256868017353049,"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."}}