{"id":"W4237102633","doi":"10.1007/978-981-10-1389-8_12-1","title":"Principles and Challenges of Cooperative Spectrum Sensing in Cognitive Radio Networks","year":2017,"lang":"en","type":"book-chapter","venue":"","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Cognitive radio; Computer science; Overhead (engineering); Leverage (statistics); Computer network; Key (lock); Spectrum management; Diversity gain; Wireless; Software deployment; Cascading Style Sheets; Telecommunications; MIMO; Channel (broadcasting); Computer security; 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"],"consensus_categories":[],"category_scores_codex":[0.0004154604,0.0004882928,0.0009046977,0.0002786571,0.0001785719,0.000190189,0.0003501652,0.0003420305,0.00002322594],"category_scores_gemma":[0.00005817288,0.0004591276,0.0001163901,0.0000318459,0.0003743085,0.0002867172,0.0004732965,0.0006023655,0.000003501811],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000683211,"about_ca_system_score_gemma":0.0001144806,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004903358,"about_ca_topic_score_gemma":0.002093953,"domain_scores_codex":[0.9978761,0.00006002161,0.0005010035,0.0008851995,0.0002590916,0.0004185527],"domain_scores_gemma":[0.9981876,0.0005135423,0.0004729798,0.0005423401,0.0001639502,0.000119581],"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":[0.00002676179,0.00001496111,0.00001941329,0.00003852136,0.0001330505,0.0003414183,0.0006468085,0.0001412042,0.000002779886,0.8027385,0.00001993942,0.1958767],"study_design_scores_gemma":[0.004268974,0.001269561,0.006277338,0.01381773,0.0002662232,0.00116435,0.0003622718,0.8660012,0.0005526943,0.07730742,0.02444352,0.004268712],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.000153592,0.01708467,0.08672021,0.001010156,0.0003102805,0.0005056558,0.000006362072,0.00007704647,0.894132],"genre_scores_gemma":[0.8282058,0.0649645,0.005404216,0.0001849585,0.0008796605,0.000002637685,0.00001525564,0.0001218836,0.1002211],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.86586,"threshold_uncertainty_score":0.999786,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05075777578116687,"score_gpt":0.2532639083319114,"score_spread":0.2025061325507446,"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."}}