{"id":"W2052081497","doi":"10.1155/2009/635947","title":"Improving Sensing Accuracy in Cognitive PANs through Modulation of Sensing Probability","year":2009,"lang":"en","type":"article","venue":"Mobile Information Systems","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Computer science; Cognitive radio; Probabilistic logic; Duty cycle; Channel (broadcasting); Set (abstract data type); Modulation (music); Range (aeronautics); Selection (genetic algorithm); Algorithm; Data mining; Machine learning; Artificial intelligence; Telecommunications; Wireless; Power (physics)","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":[0.002975408,0.0005864708,0.0009034764,0.0006746902,0.0004682942,0.001018598,0.0009408355,0.0007338071,0.0004369179],"category_scores_gemma":[0.02657344,0.0003981692,0.0002905102,0.0007863606,0.00105931,0.001927725,0.001325483,0.0007478484,0.0001621879],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005635924,"about_ca_system_score_gemma":0.0006436203,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000648072,"about_ca_topic_score_gemma":0.0005901834,"domain_scores_codex":[0.9979429,0.0007167708,0.0001583339,0.0003567719,0.0006219062,0.0002032829],"domain_scores_gemma":[0.9852465,0.01136388,0.001118161,0.001377714,0.0007497825,0.0001440013],"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":[0.0007712471,0.0001764393,0.005643328,0.0001649537,0.00008150355,0.0002115245,0.0004752123,0.6466966,0.04899901,0.03140884,0.0006867789,0.2646846],"study_design_scores_gemma":[0.00004446472,0.0002197395,0.001547303,0.00002189713,0.00002762592,0.0002144794,0.00003548754,0.9760468,0.008456551,0.01269772,0.0006604473,0.00002740249],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08684655,0.0004023846,0.9101143,0.0002488749,0.00004040102,0.00004528161,0.00001627126,0.0003233142,0.001962693],"genre_scores_gemma":[0.9019539,0.0002450993,0.09713049,0.00007677102,0.00007734702,0.0000560737,0.00001185561,0.00002688583,0.0004215886],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002975408,"threshold_uncertainty_score":0.01573563,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01914781371440347,"score_gpt":0.2572499701842464,"score_spread":0.2381021564698429,"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."}}