{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003474271,0.0001633615,0.0002990654,0.0001121047,0.0001586111,0.00003170599,0.0001004499,0.00007185437,0.000004874575],"category_scores_gemma":[0.0001590794,0.0001605477,0.00007979501,0.000755491,0.0001371567,0.0005902525,0.0001008499,0.0001848218,0.000002386423],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009908614,"about_ca_system_score_gemma":0.00009531083,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007481584,"about_ca_topic_score_gemma":0.0002815397,"domain_scores_codex":[0.9982839,0.0001655955,0.0004360968,0.0005121334,0.0002596107,0.0003426598],"domain_scores_gemma":[0.9989933,0.0003134624,0.0001446768,0.0002794688,0.0002239894,0.00004503782],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001690847,0.0005135547,0.01805717,0.0001221821,0.0001270327,0.0007199253,0.05680216,0.03899628,0.04300795,0.01359253,0.0001319458,0.8277602],"study_design_scores_gemma":[0.0004688079,0.00005347343,0.03886148,0.000244808,0.000006326283,0.0002197708,0.0002292412,0.9402472,0.01123786,0.008183652,0.000006611527,0.000240771],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5343531,0.00002079151,0.4616235,0.0001617953,0.00009331408,0.0001644724,4.599799e-7,0.00006097246,0.003521541],"genre_scores_gemma":[0.8765063,0.0000106133,0.123293,0.00008975244,0.00006299816,2.348823e-7,0.000002175679,0.000008949398,0.00002594119],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9012509,"threshold_uncertainty_score":0.6546943,"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."}}