{"id":"W2766207463","doi":"10.1002/dac.3443","title":"A survey of overlay and underlay paradigms in cognitive radio networks","year":2017,"lang":"en","type":"article","venue":"International Journal of Communication Systems","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":45,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Underlay; Cognitive radio; Computer science; Computer network; Overlay; Throughput; Wireless; Transmission (telecommunications); Overlay network; Software-defined radio; Wireless network; Telecommunications; The Internet; Signal-to-noise ratio (imaging); World Wide Web","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":[],"consensus_categories":[],"category_scores_codex":[0.001824236,0.00008998818,0.0002829039,0.0001910816,0.0001119084,0.0004213481,0.001471356,0.0000469379,0.000001398494],"category_scores_gemma":[0.0003682456,0.0000831605,0.00005151819,0.00008828237,0.0001263834,0.0007052526,0.0002582919,0.0002331907,4.605371e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006789155,"about_ca_system_score_gemma":0.00007681386,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008725681,"about_ca_topic_score_gemma":0.0003558674,"domain_scores_codex":[0.9983945,0.0004189008,0.0006177811,0.0001095673,0.0003531802,0.0001060831],"domain_scores_gemma":[0.9963662,0.0009377304,0.001312225,0.000426346,0.0009029821,0.00005451917],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005202073,0.000500368,0.6615021,0.00001250855,0.00112316,0.0001985144,0.004227867,0.02697327,0.0001390495,0.1006435,0.0008657771,0.2032937],"study_design_scores_gemma":[0.0009847202,0.00005296582,0.7413832,0.0006619567,0.000006573824,0.0001742083,0.00007984077,0.2559075,0.00002069342,0.0003974612,0.0002385645,0.00009233507],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4894433,0.01284953,0.488197,0.001791668,0.00186024,0.0002606505,0.00001197281,0.00001439232,0.005571275],"genre_scores_gemma":[0.997663,0.001819054,0.0003742323,0.00002942588,0.00008557519,0.000001176549,0.000003185023,0.000005568465,0.00001879505],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5082197,"threshold_uncertainty_score":0.406307,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05036733424563223,"score_gpt":0.3269766796008684,"score_spread":0.2766093453552362,"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."}}