{"id":"W2948949321","doi":"10.1109/tnsm.2019.2946949","title":"Probabilistic Virtual Link Embedding Under Demand Uncertainty","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Testbed; Probabilistic logic; Embedding; Mathematical optimization; Bandwidth (computing); Distributed computing; TRACE (psycholinguistics); Optimization problem; Network congestion; Algorithm; Computer network; Network packet; Mathematics; Artificial intelligence","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":[0.001694677,0.000916604,0.001028909,0.0004640009,0.0003885705,0.001158804,0.00117835,0.000943759,0.001393715],"category_scores_gemma":[0.007733404,0.0005835507,0.0004355818,0.0008139632,0.001030968,0.003199935,0.001455719,0.001353407,0.0001334773],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00095491,"about_ca_system_score_gemma":0.0007658894,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002237113,"about_ca_topic_score_gemma":0.001490171,"domain_scores_codex":[0.9987056,0.0005766225,0.0000403255,0.0002090891,0.0003006181,0.0001677035],"domain_scores_gemma":[0.9955635,0.003395267,0.0003454965,0.0002870789,0.0003005983,0.000108013],"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":[0.00002075304,0.000007419657,0.00009835464,0.00001333154,0.000005073959,0.0000223728,0.000009648133,0.9901459,0.0002718706,0.005861274,0.00009678077,0.003447217],"study_design_scores_gemma":[0.000001975707,0.00000766526,0.00002840184,0.000001322655,0.000001389027,0.000008477471,0.000005576069,0.9950593,0.0001819491,0.004606368,0.00009555364,0.000002165104],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03361708,0.0001810958,0.9644527,0.0001571873,0.00002174342,0.0000241488,0.00005775829,0.00009408737,0.001394209],"genre_scores_gemma":[0.9326053,0.0003013614,0.06499589,0.00006076557,0.00003778407,0.00007414593,0.0001411608,0.00006193074,0.001721713],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002237113,"threshold_uncertainty_score":0.008962452,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01093639563255452,"score_gpt":0.2223175059527623,"score_spread":0.2113811103202078,"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."}}