{"id":"W2122520867","doi":"10.1109/glocom.2009.5425802","title":"Relay Based Cooperative Spectrum Sensing in Cognitive Radio Networks","year":2009,"lang":"en","type":"article","venue":"","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":92,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Cognitive radio; Relay; Rayleigh fading; Computer science; False alarm; Fading; Detector; Computer network; Cognitive network; Relay channel; Cognition; Wireless; Telecommunications; Electronic engineering; Engineering; Channel (broadcasting); Artificial intelligence; Power (physics); Psychology; 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":[0.001531993,0.0005708359,0.0006784089,0.0004599625,0.0003458407,0.0008735254,0.001054466,0.00101884,0.0005314446],"category_scores_gemma":[0.003858333,0.0003259192,0.0003379472,0.0004749971,0.0009191236,0.001226664,0.0007557682,0.0004175454,0.0001825247],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000495749,"about_ca_system_score_gemma":0.0003227773,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001038299,"about_ca_topic_score_gemma":0.0006480473,"domain_scores_codex":[0.9989911,0.0004558262,0.00003428377,0.000150111,0.0002799568,0.00008871771],"domain_scores_gemma":[0.9982042,0.001310736,0.0001357723,0.0001482553,0.0001694156,0.00003150186],"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":[0.0003887665,0.00008190486,0.00109452,0.0003166847,0.0001658356,0.0009147255,0.0005573807,0.802103,0.02359094,0.09321182,0.001229164,0.07634514],"study_design_scores_gemma":[0.00002270312,0.0001001751,0.0001631093,0.0000128036,0.00003422875,0.0002541031,0.00003404264,0.9700771,0.002585354,0.02568381,0.001014907,0.00001757406],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04965103,0.002084827,0.9443742,0.0001369229,0.00005181785,0.00002871854,0.00002094699,0.0002337121,0.003417799],"genre_scores_gemma":[0.961709,0.0009386862,0.03627704,0.0000679923,0.00004290384,0.00004915288,0.00001367617,0.00001279463,0.0008886789],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001531993,"threshold_uncertainty_score":0.008102,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01348044062725041,"score_gpt":0.2425016647093538,"score_spread":0.2290212240821034,"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."}}