{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002811153,0.0002442384,0.0002260838,0.0000934794,0.0003004749,0.0002067377,0.0004402809,0.00008949051,0.00006070118],"category_scores_gemma":[3.990526e-7,0.0002229467,0.00007318743,0.0006653623,0.00002085661,0.0002207151,0.00002069713,0.0002300857,0.0001352093],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005702664,"about_ca_system_score_gemma":0.00001834008,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002782952,"about_ca_topic_score_gemma":0.00009835322,"domain_scores_codex":[0.9983455,0.00007074103,0.0002588165,0.0005930283,0.000275879,0.0004560178],"domain_scores_gemma":[0.9989787,0.0001827275,0.00006396268,0.0005983178,0.00004925726,0.0001270537],"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":[0.00002628916,0.00005145893,0.00001642297,0.0000622192,0.00008192004,0.000005586774,0.0001501035,0.9175036,0.00000137273,0.01269452,0.0002817647,0.06912476],"study_design_scores_gemma":[0.0008814735,0.0002238453,0.0004780623,0.0001635832,0.00007069758,0.000007702048,0.0001402054,0.9853904,0.00000638554,0.005032842,0.007215887,0.0003889619],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01438171,0.00008885378,0.9792244,0.001940852,0.001351134,0.000554245,0.000001851195,0.0002851909,0.002171746],"genre_scores_gemma":[0.9877971,0.0003598066,0.006287536,0.004355192,0.000165356,0.00007228157,0.00000311378,0.00002421105,0.0009354002],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9734154,"threshold_uncertainty_score":0.9091502,"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."}}