{"id":"W1547744641","doi":"10.1109/icassp.2015.7178552","title":"Accurate kernel-based spectrum sensing for Gaussian and non-Gaussian noise models","year":2015,"lang":"en","type":"article","venue":"","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Gaussian; Gaussian noise; Cognitive radio; Detector; Kernel (algebra); Computer science; Gaussian function; Noise (video); Algorithm; Energy (signal processing); Nonlinear system; Spectrum (functional analysis); Artificial intelligence; Mathematics; Telecommunications; Statistics; Physics; 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":[0.0009313968,0.0004824182,0.0007047968,0.0004120898,0.000237247,0.0007884643,0.000879209,0.000799898,0.0006635999],"category_scores_gemma":[0.004326623,0.0002328064,0.0003442243,0.0003509369,0.0007268854,0.001880635,0.001223821,0.0005776999,0.0002528411],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003921818,"about_ca_system_score_gemma":0.0004980338,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009027356,"about_ca_topic_score_gemma":0.0008589518,"domain_scores_codex":[0.9994175,0.0001875286,0.00003649201,0.0001043574,0.0001924859,0.00006163077],"domain_scores_gemma":[0.9988092,0.0005968941,0.0001347082,0.0002700804,0.0001500869,0.00003899615],"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":[0.0004790719,0.0001104589,0.001411362,0.0001966354,0.00007892148,0.0002642393,0.000164567,0.7125551,0.0385757,0.1002331,0.0009063779,0.1450245],"study_design_scores_gemma":[0.000002423191,0.00001408361,0.00009331013,0.000001938595,0.000001944607,0.00005049918,0.000005610329,0.9921605,0.002098347,0.005409629,0.0001539594,0.000007734069],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02333158,0.0001303731,0.9756519,0.00004167368,0.00001644813,0.000007271419,0.00001594236,0.0001849605,0.0006198701],"genre_scores_gemma":[0.8784498,0.0001673867,0.1201948,0.00004316289,0.000018755,0.0000151715,0.00006349928,0.0000319085,0.001015581],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0009313968,"threshold_uncertainty_score":0.004925728,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03345277198213202,"score_gpt":0.2586016542621513,"score_spread":0.2251488822800192,"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."}}