{"id":"W2011163394","doi":"10.1109/glocom.2011.6133935","title":"Probabilistic Analysis of Mutual Interference in Cognitive Radio Communications","year":2011,"lang":"en","type":"article","venue":"","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Cognitive radio; Interference (communication); Computer science; Transmission (telecommunications); Co-channel interference; Computer network; Telecommunications; Channel (broadcasting); 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":[0.0002198384,0.00008252128,0.0002375795,0.0003781825,0.00003538303,0.0000216358,0.0006423047,0.00002934833,0.00006539494],"category_scores_gemma":[0.00008611137,0.00007500717,0.00008021895,0.001810134,0.0001534145,0.0001747521,0.0002734842,0.0001134351,0.000004993376],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002864604,"about_ca_system_score_gemma":0.0000376242,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003260855,"about_ca_topic_score_gemma":0.002850362,"domain_scores_codex":[0.999153,0.0001111907,0.000269487,0.0002263034,0.00008260064,0.000157381],"domain_scores_gemma":[0.9988424,0.0003578664,0.00008104773,0.0005508609,0.0001259246,0.00004194641],"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":[0.00007107231,0.001168834,0.07784379,0.00001624855,0.001254457,0.00003755138,0.03263246,0.0001498621,0.0002275298,0.6941402,0.00005138453,0.1924066],"study_design_scores_gemma":[0.0002494414,0.000105247,0.1505684,0.00006079039,0.0001771306,0.000004539899,0.0003552974,0.8434919,0.0004076496,0.004406314,0.000006036772,0.0001673529],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1798802,0.0001232586,0.7600951,0.00009349739,0.00004618781,0.0001806314,0.000003913061,0.0000581044,0.05951909],"genre_scores_gemma":[0.9863625,0.00002927901,0.01350162,0.00004818535,0.000004141085,0.000005011206,0.000004064016,0.000002663407,0.0000426124],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8433419,"threshold_uncertainty_score":0.3058704,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0697669680034978,"score_gpt":0.285284875305701,"score_spread":0.2155179073022032,"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."}}