{"id":"W2136415151","doi":"10.1109/sarnof.2012.6222715","title":"Capacity-interference investigation in cognitive radio networks with beacon","year":2012,"lang":"en","type":"article","venue":"","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Cognitive radio; Interference (communication); Computer science; Channel (broadcasting); Transmission (telecommunications); Computer network; Orthogonal frequency-division multiplexing; Fading; Co-channel interference; Telecommunications; 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":[],"consensus_categories":[],"category_scores_codex":[0.0003235736,0.0001555628,0.0001687644,0.0001142322,0.00007424822,0.000109195,0.0001784473,0.00005997174,0.0000191737],"category_scores_gemma":[0.00002567296,0.0001261099,0.00002633452,0.0005456491,0.00009397407,0.0009180846,0.00006524564,0.0002541779,0.00001116089],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006628256,"about_ca_system_score_gemma":0.0000296714,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001171884,"about_ca_topic_score_gemma":0.0003479656,"domain_scores_codex":[0.9988127,0.0001041683,0.0001736263,0.000283814,0.0001468934,0.0004787921],"domain_scores_gemma":[0.9993258,0.0002095055,0.00006454629,0.0001683937,0.0000694481,0.0001623477],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001295586,0.000320627,0.562403,0.00002209263,0.0001156522,0.00007692365,0.01218348,0.002657736,0.0006013558,0.157811,0.0008404517,0.2628381],"study_design_scores_gemma":[0.001386024,0.0003011064,0.3181782,0.0004664226,0.00002215604,0.0002133529,0.0002872489,0.6723446,0.004353945,0.001578547,0.0001190044,0.0007494222],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3319766,0.000121366,0.6603605,0.0002237679,0.0001452581,0.0001339668,2.467088e-7,0.00008244586,0.006955924],"genre_scores_gemma":[0.989288,0.00001410433,0.009885669,0.0005171489,0.0001858258,0.000007137761,0.000003144019,0.000008618796,0.00009035441],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6696869,"threshold_uncertainty_score":0.5142612,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02864574851306705,"score_gpt":0.2316924275807559,"score_spread":0.2030466790676889,"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."}}