{"id":"W1990376256","doi":"10.1016/j.comnet.2010.12.027","title":"QoS capacity of virtual wireless networks","year":2011,"lang":"en","type":"article","venue":"Computer Networks","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"Korea Science and Engineering Foundation","keywords":"Computer science; Cognitive radio; Computer network; Quality of service; Wireless; Wireless network; Markov chain; Service provider; Radio resource management; Service (business); Distributed computing; Telecommunications","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":[0.002339969,0.0005773709,0.0009449539,0.00142169,0.001045746,0.003249475,0.001521833,0.0009200987,0.006995125],"category_scores_gemma":[0.01900272,0.0005114458,0.0002350811,0.001427223,0.001596479,0.004455138,0.00167476,0.001135443,0.0005233016],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001746288,"about_ca_system_score_gemma":0.001003238,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001883896,"about_ca_topic_score_gemma":0.0008760456,"domain_scores_codex":[0.9986258,0.0004758745,0.00005284827,0.0001578149,0.0003301074,0.0003575851],"domain_scores_gemma":[0.9809579,0.0147088,0.0007200492,0.001126905,0.001794241,0.0006922879],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003857162,0.00008207071,0.0007996457,0.0002047462,0.00004371939,0.0001439945,0.0002821067,0.3480927,0.008137816,0.6016517,0.006439915,0.03373584],"study_design_scores_gemma":[0.00001922325,0.00003085549,0.0003558932,0.00004470589,0.00001748402,0.0001108627,0.00009529719,0.732599,0.001685008,0.2624963,0.002524962,0.00002033723],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4361275,0.005786172,0.4803603,0.007266735,0.0005665619,0.0000682596,0.001316655,0.0009863897,0.06752142],"genre_scores_gemma":[0.9917604,0.0009253204,0.004371549,0.0001541322,0.0001996946,0.00005413211,0.0001198552,0.00005932562,0.002355503],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006995125,"threshold_uncertainty_score":0.02340096,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02561916448884732,"score_gpt":0.2046007411772566,"score_spread":0.1789815766884093,"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."}}