{"id":"W2093987524","doi":"10.1109/vtcspring.2014.7022808","title":"Adaptive Grouping Scheme for Cooperative Spectrum Sensing in Cognitive Radio Networks","year":2014,"lang":"en","type":"article","venue":"","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Cognitive radio; Computer science; Throughput; False alarm; Overhead (engineering); Scheme (mathematics); Maximization; Efficient energy use; Signal-to-noise ratio (imaging); Spectral efficiency; Real-time computing; Algorithm; Computer network; Mathematical optimization; Wireless; Artificial intelligence; Telecommunications; Engineering; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007231096,0.0003097869,0.0004426786,0.0002036925,0.0002846742,0.0002692425,0.0002643494,0.0001162515,0.00001085637],"category_scores_gemma":[0.000173906,0.0002986128,0.0001238729,0.0007436527,0.00009661417,0.0005399661,0.0001555048,0.0003385958,0.000008148646],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001338863,"about_ca_system_score_gemma":0.00005882743,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000769484,"about_ca_topic_score_gemma":0.0005867817,"domain_scores_codex":[0.99766,0.0001971062,0.0003748059,0.0007901416,0.0001919382,0.0007859651],"domain_scores_gemma":[0.9980153,0.00128787,0.0001180673,0.0002621863,0.0001774825,0.0001390413],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003878435,0.0002167881,0.001173159,0.00002239285,0.0002462385,0.0001304246,0.002967827,0.01903537,0.0005550591,0.5596803,0.00115597,0.4144286],"study_design_scores_gemma":[0.001388585,0.0002884647,0.001131796,0.0001920409,0.000009902094,0.00004369231,0.0002206018,0.991026,0.0008281863,0.004219765,0.0002124632,0.0004384622],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01081193,0.0001318276,0.977388,0.000641892,0.000377143,0.0006153053,0.000001634191,0.0001852705,0.009847053],"genre_scores_gemma":[0.921478,0.00001853927,0.07690743,0.0009034519,0.0005099608,0.000008495274,0.000006935872,0.00002600925,0.0001411853],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9719906,"threshold_uncertainty_score":0.9999466,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02137595523990632,"score_gpt":0.2483270927729108,"score_spread":0.2269511375330044,"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."}}