{"id":"W1997278062","doi":"10.5555/1535571.1535612","title":"Reducing sensing error in cognitive PANs through differential sensing","year":2008,"lang":"en","type":"article","venue":"","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Idle; Channel (broadcasting); Differential (mechanical device); Cognitive radio; Probabilistic logic; Set (abstract data type); Computer science; Real-time computing; Data mining; Telecommunications; Engineering; Artificial intelligence; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005573121,0.0009273916,0.00143844,0.0008638547,0.0007730843,0.001599943,0.002057719,0.001057739,0.0006267274],"category_scores_gemma":[0.02621259,0.0008142262,0.0004665231,0.0008400332,0.002381229,0.003019131,0.003205051,0.001171797,0.0001417078],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001109201,"about_ca_system_score_gemma":0.0009800115,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001238088,"about_ca_topic_score_gemma":0.0009149292,"domain_scores_codex":[0.9962115,0.001198734,0.0002101202,0.0006958414,0.001052973,0.0006307581],"domain_scores_gemma":[0.9761176,0.01711857,0.002544163,0.00222628,0.001420479,0.0005729806],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006765492,0.0001294242,0.0046587,0.000142542,0.0001211998,0.0004502334,0.0003932479,0.891448,0.01157578,0.03788991,0.0005538716,0.05196062],"study_design_scores_gemma":[0.00002864431,0.0001794443,0.0008050238,0.0000162372,0.00002671181,0.0001866209,0.00007016688,0.9701157,0.002111893,0.0261405,0.0002979552,0.00002108217],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1423279,0.0004437928,0.8542882,0.0003347066,0.00004790792,0.00004760673,0.00003361318,0.0002217002,0.002254576],"genre_scores_gemma":[0.9849404,0.0001142029,0.01445553,0.00005366136,0.00002544697,0.000027623,0.00001235086,0.000014763,0.000356035],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005573121,"threshold_uncertainty_score":0.02947384,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0418871750251633,"score_gpt":0.2729086260688847,"score_spread":0.2310214510437213,"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."}}