{"id":"W2133221370","doi":"10.1109/ccece.2009.5090120","title":"Framework for performance evaluation of cognitive radio networks in heterogeneous environments","year":2009,"lang":"en","type":"article","venue":"","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Communications Research Centre Canada","funders":"","keywords":"Cognitive radio; Computer science; Cognitive network; Heterogeneous network; Computer network; Radio resource management; Distributed computing; Radio access network; State (computer science); Telecommunications; Wireless network; Wireless; Base station; Mobile station","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":[0.01096827,0.003011443,0.002128465,0.002089262,0.0007464642,0.003647253,0.004620858,0.002586595,0.002086215],"category_scores_gemma":[0.01651031,0.0006578442,0.001374207,0.001306903,0.002346159,0.002538009,0.002488359,0.002672677,0.000794474],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003155438,"about_ca_system_score_gemma":0.003446075,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00490823,"about_ca_topic_score_gemma":0.002270954,"domain_scores_codex":[0.9912272,0.004900582,0.0003516889,0.0004461058,0.002685342,0.0003890746],"domain_scores_gemma":[0.9937822,0.003836091,0.0005396538,0.0004859591,0.001118834,0.00023732],"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":[0.00004420714,0.0000913541,0.0003432897,0.00009185976,0.00005618504,0.00008685222,0.00007473161,0.8133918,0.001255672,0.1706859,0.0008260293,0.01305215],"study_design_scores_gemma":[0.00002229198,0.00006563565,0.00008113853,0.00003861222,0.00001335379,0.00002783236,0.00001732231,0.9655705,0.0002949462,0.03239092,0.001462296,0.00001514157],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00221127,0.0003791528,0.9925236,0.0002951491,0.00004207268,0.0001291993,0.000046079,0.0001459825,0.004227532],"genre_scores_gemma":[0.3626594,0.001659042,0.6299745,0.0002431499,0.0003225298,0.001757359,0.000218661,0.0001378482,0.003027516],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01096827,"threshold_uncertainty_score":0.05800647,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0262805897022452,"score_gpt":0.2791986802967085,"score_spread":0.2529180905944632,"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."}}