{"id":"W2789911982","doi":"10.1049/iet-net.2017.0004","title":"Leveraging synergy of SDWN and multi‐layer resource management for 5G networks","year":2018,"lang":"en","type":"article","venue":"IET Networks","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; McGill University","funders":"","keywords":"Computer science; Software-defined networking; Distributed computing; Virtualization; Reliability (semiconductor); Computer network; Resource management (computing); Wireless network; Layer (electronics); Resource allocation; Key (lock); Wireless; Cloud computing; Telecommunications; Operating system","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":[0.0005103103,0.000251627,0.0003159904,0.00009585076,0.0002575228,0.0001397244,0.0007113481,0.0001649114,0.000009220216],"category_scores_gemma":[0.0000148986,0.0002371691,0.000106205,0.0004694631,0.0001386232,0.0001817357,0.0003249095,0.0001763146,0.000002961224],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000261489,"about_ca_system_score_gemma":0.00001175809,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002359366,"about_ca_topic_score_gemma":0.00001513318,"domain_scores_codex":[0.9980847,0.00006392719,0.0003852352,0.0006210514,0.0001991774,0.0006459642],"domain_scores_gemma":[0.9985447,0.0003034421,0.000179444,0.0007200269,0.0001085778,0.000143829],"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":[0.0001264817,0.0001354447,0.002408511,0.00007671354,0.0002401085,0.00002421117,0.0006895504,0.3844461,0.000009461937,0.01961642,0.0706805,0.5215465],"study_design_scores_gemma":[0.000841817,0.0001506598,0.002413516,0.0001317933,0.00002624537,0.000009919767,0.00002647054,0.9570819,0.00003015062,0.0006659136,0.03833951,0.0002820695],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001977724,0.001789947,0.9930915,0.0002243944,0.0007818347,0.0003515075,9.197279e-7,0.0001975472,0.00158459],"genre_scores_gemma":[0.9290074,0.0002250835,0.06796631,0.001141289,0.000950686,0.00005831445,0.000007668971,0.00003852626,0.0006047932],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9270296,"threshold_uncertainty_score":0.9671475,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02209963314952423,"score_gpt":0.2448393065647557,"score_spread":0.2227396734152315,"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."}}