{"id":"W1980031634","doi":"10.1109/glocom.2008.ecp.998","title":"Reducing Sensing Error in Cognitive PANs through Modulation of Sensing Probability","year":2008,"lang":"en","type":"article","venue":"","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Cognitive radio; Computer science; Probabilistic logic; Idle; Channel (broadcasting); Set (abstract data type); Range (aeronautics); Reduction (mathematics); Probability of error; Real-time computing; Modulation (music); Data mining; Electronic engineering; Telecommunications; Artificial intelligence; Algorithm; Engineering; Wireless; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00312313,0.0007024753,0.0009332226,0.0007629439,0.0006165648,0.001301876,0.0009821932,0.0007030724,0.0005832324],"category_scores_gemma":[0.02837444,0.0005560923,0.0002632235,0.0006946881,0.001548513,0.002307151,0.001786542,0.0008872282,0.0001493359],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007938021,"about_ca_system_score_gemma":0.0008037974,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007322957,"about_ca_topic_score_gemma":0.0006981615,"domain_scores_codex":[0.9978344,0.0008044914,0.0001217899,0.0003514526,0.0006064939,0.0002813392],"domain_scores_gemma":[0.9749068,0.0204071,0.002017042,0.001378485,0.001002862,0.0002877355],"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":[0.0007930411,0.0001391335,0.00462098,0.0001261304,0.00008886083,0.0002106902,0.0003996157,0.831692,0.0182804,0.05416431,0.0006065133,0.08887834],"study_design_scores_gemma":[0.000035161,0.0002075328,0.001442021,0.00002241514,0.00003163255,0.0001414685,0.00005829847,0.9595675,0.003256521,0.0347948,0.0004127884,0.00002989451],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1449665,0.0006891832,0.8494033,0.0005335892,0.00005849058,0.00004287354,0.00003357204,0.0003332939,0.003939121],"genre_scores_gemma":[0.9819456,0.0001974935,0.01735916,0.00004950592,0.00005459103,0.00002732892,0.000008304399,0.00001817562,0.0003397884],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00312313,"threshold_uncertainty_score":0.01651692,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05665785403024125,"score_gpt":0.2786986927486986,"score_spread":0.2220408387184574,"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."}}