{"id":"W2058910857","doi":"10.1109/wcnc.2013.6555039","title":"Effective capacity optimization based on overlay cognitive radio network in gamma fading environment","year":2013,"lang":"en","type":"article","venue":"","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Cognitive radio; Quality of service; Overlay; Computer science; Computer network; Interference (communication); Fading; Transmitter power output; Scheme (mathematics); Cognitive network; Telecommunications; Transmitter; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006713796,0.0006266626,0.0006174148,0.0006092794,0.0003584522,0.0009181739,0.0007197075,0.0004738514,0.000863758],"category_scores_gemma":[0.001874312,0.0002015705,0.0002588056,0.0006535047,0.0009163696,0.0008838927,0.0008361888,0.0003696695,0.00008336898],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001427724,"about_ca_system_score_gemma":0.0009047562,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004032901,"about_ca_topic_score_gemma":0.002900799,"domain_scores_codex":[0.9995446,0.0001630699,0.00001104039,0.00005257568,0.0001056297,0.0001231581],"domain_scores_gemma":[0.9992095,0.0005229487,0.00006837893,0.00003567993,0.0001127676,0.00005063747],"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":[0.00004195677,0.00001730483,0.0001776823,0.00003080406,0.00001473647,0.00008283826,0.00004068595,0.9736423,0.003066332,0.01537522,0.0003083708,0.007201782],"study_design_scores_gemma":[0.000003061239,0.00001364217,0.0000418268,0.000001992974,0.000003726372,0.00001356716,0.00001085293,0.9961079,0.000388017,0.003299571,0.0001122568,0.000003602057],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.201737,0.001082456,0.7860354,0.0002354662,0.00004044851,0.0000427806,0.00006957733,0.0001973316,0.01055947],"genre_scores_gemma":[0.9853433,0.000239318,0.01369924,0.00002366588,0.000009475224,0.00002215801,0.00001444653,0.00001194065,0.0006364142],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004032901,"threshold_uncertainty_score":0.01035893,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008227121921543703,"score_gpt":0.1931564478503606,"score_spread":0.1849293259288169,"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."}}