{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000690421,0.0001671909,0.0003068749,0.0001869584,0.0001143827,0.0002717522,0.0001282361,0.00009342461,7.763525e-7],"category_scores_gemma":[0.0002205416,0.0001655415,0.00006704818,0.000685767,0.00003342677,0.003902695,0.00004992117,0.0001640459,0.00000658157],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001684001,"about_ca_system_score_gemma":0.00007940966,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004047353,"about_ca_topic_score_gemma":0.00002097219,"domain_scores_codex":[0.9981065,0.0001426841,0.0008945346,0.0002197654,0.0003433533,0.0002931566],"domain_scores_gemma":[0.9983989,0.0002937423,0.0005590203,0.0002869638,0.0004192342,0.0000421237],"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":[0.00002567264,0.00003623357,0.0003329665,0.000104713,0.0000115949,0.000005708781,0.011798,0.0452067,0.00184997,0.002923265,0.00001214887,0.937693],"study_design_scores_gemma":[0.0004981343,0.00009572772,0.004298388,0.0003651893,0.000004931306,0.00005134585,0.001028236,0.9911806,0.001364138,0.0007962143,0.0001188747,0.000198205],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3614939,0.00004267202,0.6357139,0.00003325999,0.0002339975,0.0007215544,0.000002975232,0.00008794713,0.001669795],"genre_scores_gemma":[0.9932415,0.00000607735,0.006557418,0.00009727178,0.00007140261,0.000003054471,0.00001594091,0.000004211135,0.000003071134],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9459739,"threshold_uncertainty_score":0.6750585,"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."}}