{"id":"W2010676865","doi":"10.1023/a:1019810526535","title":"DORA: Efficient Routing for MPLS Traffic Engineering","year":2002,"lang":"en","type":"article","venue":"Journal of Network and Systems Management","topic":"Advanced Optical Network Technologies","field":"Engineering","cited_by":75,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Computer network; Multiprotocol Label Switching; Bandwidth (computing); Routing (electronic design automation); Distributed computing; Path (computing); Computation; Quality of service; Algorithm","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":[0.0007922767,0.0008211257,0.0006459198,0.00081608,0.0005514672,0.001058126,0.001441366,0.000584532,0.00759001],"category_scores_gemma":[0.001288702,0.0004298298,0.0003975749,0.0003688867,0.0004260052,0.001025445,0.001253234,0.001066127,0.002785822],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005294903,"about_ca_system_score_gemma":0.0006145079,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007753168,"about_ca_topic_score_gemma":0.002203518,"domain_scores_codex":[0.9995561,0.0000992063,0.00001915098,0.00006255353,0.0002028254,0.00006015009],"domain_scores_gemma":[0.9995068,0.0001416351,0.00003801849,0.0001860816,0.00008193492,0.00004547163],"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":[0.001171695,0.0004169028,0.001213084,0.0004316336,0.0001547315,0.0002362281,0.0001434247,0.08769276,0.09566361,0.0673134,0.08070932,0.6648532],"study_design_scores_gemma":[0.0002511708,0.0001691083,0.0003928109,0.00002113366,0.00004422141,0.0002368507,0.00002718428,0.8872471,0.0437365,0.02144522,0.04637533,0.00005343759],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02170833,0.0006514584,0.9123157,0.0004733625,0.0005133415,0.0002173164,0.0006341116,0.04912311,0.01436321],"genre_scores_gemma":[0.2155641,0.0004618587,0.7606055,0.0004015035,0.0002160824,0.000226739,0.001462741,0.002559642,0.01850184],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00759001,"threshold_uncertainty_score":0.02539104,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01313260753362974,"score_gpt":0.1960834935559997,"score_spread":0.18295088602237,"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."}}