{"id":"W2146059050","doi":"10.1109/ccnc.2007.206","title":"Optimization of Spectrum Sensing for Opportunistic Spectrum Access in Cognitive Radio Networks","year":2007,"lang":"en","type":"article","venue":"","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":261,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Cognitive radio; Computer science; Spectrum (functional analysis); Computer network; Telecommunications; Wireless; Physics","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":[0.0007891147,0.00102964,0.0007659975,0.0004229523,0.000382768,0.0009304091,0.001036469,0.0008363767,0.000954297],"category_scores_gemma":[0.002104553,0.0004345673,0.0003230112,0.0005010706,0.001005507,0.000812573,0.001158621,0.0006289545,0.0001534175],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007797821,"about_ca_system_score_gemma":0.001262801,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003007292,"about_ca_topic_score_gemma":0.003246531,"domain_scores_codex":[0.9993161,0.0001969751,0.00002190304,0.0001190206,0.000178284,0.000167612],"domain_scores_gemma":[0.99909,0.0005851589,0.000161124,0.00002552115,0.00009186341,0.00004642638],"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":[0.00007924965,0.00008479226,0.0005627448,0.0001235783,0.00004472288,0.0001341987,0.00006932246,0.9616602,0.005335005,0.01235748,0.0006777816,0.01887088],"study_design_scores_gemma":[0.00000727842,0.00005156964,0.000165097,0.000008231202,0.000009452703,0.00002772475,0.00001837892,0.9945715,0.0004730694,0.004373146,0.0002865992,0.000007864747],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05546338,0.001981972,0.9312129,0.00035879,0.00008621974,0.00009045356,0.00007999233,0.0001460542,0.01058018],"genre_scores_gemma":[0.9769241,0.0005545974,0.02132899,0.00006570877,0.00003798054,0.00007217564,0.00002237139,0.00001375393,0.000980386],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003007292,"threshold_uncertainty_score":0.005979598,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0338014411272304,"score_gpt":0.2914085019816799,"score_spread":0.2576070608544495,"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."}}