{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002976378,0.001066163,0.001227811,0.0007317704,0.0008132472,0.001401845,0.001936964,0.000899252,0.001383291],"category_scores_gemma":[0.00705061,0.0005060534,0.0007599063,0.0008030747,0.001109695,0.00271812,0.001282613,0.001451043,0.0001604301],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001813547,"about_ca_system_score_gemma":0.002776072,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003757742,"about_ca_topic_score_gemma":0.003980194,"domain_scores_codex":[0.9981328,0.0006338194,0.0000708533,0.0003667968,0.0004288753,0.0003670109],"domain_scores_gemma":[0.9962858,0.002144508,0.0005174178,0.000397209,0.0004850281,0.0001700026],"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":[0.00009709183,0.00008602098,0.0003638394,0.00004559773,0.00002995227,0.00008644813,0.00005175017,0.9339094,0.001407469,0.0434639,0.000964537,0.01949404],"study_design_scores_gemma":[0.00002029854,0.00004930201,0.00005928645,0.00000363134,0.00001039078,0.00002398674,0.000009987914,0.975966,0.0007891399,0.02249291,0.0005662169,0.000008865882],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04792183,0.0001990958,0.9480807,0.0002364729,0.00008103548,0.0001081655,0.0001084969,0.0004089352,0.002855395],"genre_scores_gemma":[0.6975444,0.0002282501,0.2971118,0.0001530871,0.00006896333,0.0001926564,0.0002007544,0.0001294988,0.004370642],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003757742,"threshold_uncertainty_score":0.01574075,"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."}}