{"id":"W4238591469","doi":"10.1002/wcm.480","title":"Spectrum sensing in cognitive radio networks: the cooperation‐processing tradeoff","year":2007,"lang":"en","type":"article","venue":"Wireless Communications and Mobile Computing","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":145,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Defense Advanced Research Projects Agency","keywords":"Cognitive radio; Computer science; Fading; Spectrum management; Software deployment; Transceiver; Telecommunications; Computer network; Radio spectrum; Spectral efficiency; Channel (broadcasting); Wireless","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":["sts"],"consensus_categories":[],"category_scores_codex":[0.00147782,0.0002088676,0.0002585592,0.0001554878,0.00130696,0.0004692302,0.0007300768,0.00007777963,0.00000128675],"category_scores_gemma":[0.00002609835,0.0001808663,0.00005333023,0.001019924,0.0002908505,0.0003047425,0.0006252867,0.00057736,0.000001257072],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007717944,"about_ca_system_score_gemma":0.00006308999,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008504279,"about_ca_topic_score_gemma":0.0006993011,"domain_scores_codex":[0.9982061,0.0002327057,0.0004953626,0.0003987273,0.0001520389,0.0005150557],"domain_scores_gemma":[0.9975291,0.001332279,0.0001786623,0.0007583397,0.0001112741,0.00009032009],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000007115256,0.00006305239,0.001035322,0.000006212289,0.00001541266,0.00001634992,0.003800307,0.003676678,0.0001060039,0.008835493,0.00001384358,0.9824242],"study_design_scores_gemma":[0.0004132797,0.00004020528,0.005712873,0.0002726753,0.00001046351,0.0001450592,0.001038337,0.991266,0.00008565478,0.0002041728,0.0005716936,0.000239659],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2007007,0.005017744,0.79177,0.0008150946,0.0001097866,0.0003395989,5.037627e-7,0.0001158716,0.001130703],"genre_scores_gemma":[0.9880726,0.0007048167,0.01066946,0.000348203,0.0001609572,0.000003716784,0.000008207407,0.00001829991,0.00001377118],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9875892,"threshold_uncertainty_score":0.9999932,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01932678470680383,"score_gpt":0.2754700733661178,"score_spread":0.256143288659314,"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."}}