{"id":"W2962751476","doi":"10.1109/glocom.2015.7417132","title":"Ultra Low-Complexity Detection of Spectrum Holes in Compressed Wideband Spectrum Sensing","year":2015,"lang":"en","type":"article","venue":"2015 IEEE Global Communications Conference (GLOBECOM)","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Undersampling; Compressed sensing; Wideband; Cognitive radio; Nyquist rate; Computer science; Block (permutation group theory); Electronic engineering; Nyquist–Shannon sampling theorem; Signal-to-noise ratio (imaging); Spectrum (functional analysis); Algorithm; Sampling (signal processing); Telecommunications; Mathematics; Wireless; Engineering; Physics; Detector","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002839478,0.0003046685,0.0004690691,0.000160079,0.0001036034,0.00009063743,0.0009709685,0.0001662888,0.00001071981],"category_scores_gemma":[0.00005678205,0.0003445031,0.00008710898,0.0005618617,0.0004765698,0.0002639295,0.000153618,0.000399117,0.00003420453],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003355054,"about_ca_system_score_gemma":0.0001128116,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002085406,"about_ca_topic_score_gemma":0.008123577,"domain_scores_codex":[0.9982599,0.0002192597,0.0005792269,0.0002648184,0.0002732269,0.000403572],"domain_scores_gemma":[0.9976045,0.00008903888,0.000161022,0.001772263,0.0002129281,0.0001602454],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0007685782,0.002517179,0.02928787,0.0005601801,0.0007794171,0.0001029269,0.004745638,0.08299743,0.6987567,0.06668551,0.02345713,0.08934142],"study_design_scores_gemma":[0.002096057,0.000194486,0.01418843,0.0009968174,0.00009077775,0.0001188744,0.0009131262,0.3796447,0.4827939,0.1145871,0.003008999,0.001366721],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7698585,0.002098339,0.1825564,0.001256587,0.0008661019,0.0008045465,0.0001381086,0.001442635,0.04097876],"genre_scores_gemma":[0.9954983,0.0004051436,0.003949465,0.00003623809,0.00004082593,0.00000766727,0.00002884747,0.00002509205,0.000008428633],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2966473,"threshold_uncertainty_score":0.9999007,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08158860634591454,"score_gpt":0.2961147823282241,"score_spread":0.2145261759823096,"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."}}