{"id":"W2149395821","doi":"10.1109/wcnc.2010.5506471","title":"Blind Spectrum Sensing in Cognitive Radio","year":2010,"lang":"en","type":"article","venue":"","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Estimator; Cognitive radio; Computer science; Noise power; Noise (video); Algorithm; SIGNAL (programming language); Minimum mean square error; Variance (accounting); Mean squared error; Signal-to-noise ratio (imaging); Power (physics); Statistics; Artificial intelligence; Mathematics; Telecommunications; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003184384,0.0001519464,0.0001831084,0.0002124771,0.00009720249,0.0002140064,0.0002248695,0.00007913628,0.0000687875],"category_scores_gemma":[0.00007113732,0.0001430209,0.00005883313,0.0006248103,0.00006604863,0.0003481021,0.0001194926,0.0004977661,0.00005638248],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002489024,"about_ca_system_score_gemma":0.0000614055,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001178583,"about_ca_topic_score_gemma":0.002447278,"domain_scores_codex":[0.9986901,0.00004473586,0.0002050022,0.000442002,0.0001815071,0.0004366617],"domain_scores_gemma":[0.9992743,0.0002597585,0.00004718489,0.0002645284,0.00004651295,0.0001077459],"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":[0.0000847089,0.0002188001,0.006985276,0.000008029423,0.00005510364,0.001364386,0.002413701,0.00005539604,0.01446285,0.16226,0.0005673363,0.8115245],"study_design_scores_gemma":[0.003953663,0.0001550948,0.04910955,0.0001267657,0.00001576921,0.000999105,0.000227583,0.8804464,0.02782468,0.03430725,0.001749954,0.001084182],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5507343,0.00003119715,0.399563,0.001586594,0.000732349,0.0001715516,4.72254e-7,0.0001657072,0.04701483],"genre_scores_gemma":[0.9802183,0.000008131547,0.01883569,0.0004149477,0.0002768096,5.435889e-7,0.00000121382,0.00001028945,0.0002341075],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.880391,"threshold_uncertainty_score":0.5832223,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01646154820978488,"score_gpt":0.2554092911909501,"score_spread":0.2389477429811653,"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."}}