{"id":"W1998710728","doi":"10.1109/tcomm.2014.2338856","title":"Performance Analysis of Relay-Based Cooperative Spectrum Sensing in Cognitive Radio Networks Over Non-Identical Nakagami-<named-content content-type=\"math\" xlink:type=\"simple\"> <inline-formula> <tex-math notation=\"TeX\">$m$</tex-math></inline-formula></named-content> Channels","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Communications","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Nakagami distribution; Cognitive radio; Fading; Relay; False alarm; Computer science; Channel (broadcasting); Algorithm; Bandwidth (computing); Spectral efficiency; Topology (electrical circuits); Mathematics; Electronic engineering; Wireless; Telecommunications; Engineering; Artificial intelligence","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","sts","research_integrity"],"consensus_categories":["metaepi_narrow"],"category_scores_codex":[0.002637927,0.001705333,0.002783685,0.002364339,0.002512543,0.0008342224,0.002711689,0.0008677915,0.00007984361],"category_scores_gemma":[0.0004886478,0.001723903,0.001417411,0.006759442,0.001276075,0.002123934,0.000212996,0.002559748,0.0001395397],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001174669,"about_ca_system_score_gemma":0.0007723933,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003681243,"about_ca_topic_score_gemma":0.009348727,"domain_scores_codex":[0.9887199,0.001433298,0.003706102,0.002147704,0.001812892,0.002180081],"domain_scores_gemma":[0.9860417,0.004063166,0.001650068,0.004315895,0.00310354,0.0008256379],"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":[0.00246034,0.007767066,0.001823703,0.0003369952,0.008602587,0.0002362933,0.01314262,0.7965477,0.03996835,0.04335209,0.0005065475,0.08525576],"study_design_scores_gemma":[0.004634601,0.0009224407,0.01070266,0.0011497,0.001812388,0.0001148785,0.0003195173,0.9744762,0.002881199,0.0001467178,0.001136313,0.00170337],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4432974,0.0007559766,0.5502945,0.001621216,0.001207792,0.001727309,0.0002158002,0.0003613396,0.0005186884],"genre_scores_gemma":[0.9827197,0.002626224,0.01151996,0.00105023,0.0003533767,0.0001584683,0.0007723169,0.0002175674,0.0005821543],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5394223,"threshold_uncertainty_score":0.9997414,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04041324042022099,"score_gpt":0.2738888044175049,"score_spread":0.2334755639972839,"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."}}