{"id":"W2082771129","doi":"10.1109/lwc.2014.2344669","title":"Analysis of Sub-Band Allocation in Multi-Service Cognitive Radio Access Networks","year":2014,"lang":"en","type":"article","venue":"IEEE Wireless Communications Letters","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Cognitive radio; Computer science; Computer network; Network packet; Quality of service; Markov process; Markov chain; Poisson distribution; Service (business); Telecommunications; Wireless; Mathematics","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":[0.0005950623,0.0001869158,0.0004289241,0.000649973,0.0001939632,0.0001696012,0.002106514,0.00008033054,0.000001775865],"category_scores_gemma":[0.00003810983,0.0002074262,0.0001312006,0.003858392,0.0001648802,0.0005501886,0.0002607167,0.0003213823,0.000003144689],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008579749,"about_ca_system_score_gemma":0.00003248204,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000635579,"about_ca_topic_score_gemma":0.006452446,"domain_scores_codex":[0.9980261,0.0005763553,0.0004999696,0.0003851432,0.0002051526,0.000307288],"domain_scores_gemma":[0.996685,0.001089546,0.0003069802,0.001599252,0.0002463796,0.00007289393],"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":[0.00007388816,0.001226381,0.1380965,0.00006621094,0.002104312,0.00001166881,0.006213247,0.5416012,0.03536588,0.00774672,0.000259277,0.2672348],"study_design_scores_gemma":[0.0005011563,0.00001002009,0.1121196,0.00009244209,0.0001490218,0.000001803611,0.0000299816,0.8854717,0.001381444,0.00001593062,0.00002479333,0.0002020799],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4014005,0.0001179365,0.5953166,0.002790352,0.00007534053,0.0001507308,0.000002423059,0.00004586112,0.0001001833],"genre_scores_gemma":[0.9943403,0.0002893152,0.003013896,0.002218275,0.00003117159,0.00002988254,0.00006044696,0.00001476036,0.000001937319],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5929398,"threshold_uncertainty_score":0.8458595,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03710038917370081,"score_gpt":0.2904872163541656,"score_spread":0.2533868271804648,"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."}}