{"id":"W2920798028","doi":"10.1109/istel.2018.8661035","title":"Blind Discrete-Time Cyclostationary Spectrum Sensing with Multiple Primary Users in Presence of Spatially and Temporally Correlated Noise","year":2018,"lang":"en","type":"article","venue":"","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Cyclostationary process; Detector; Noise (video); Computer science; Likelihood-ratio test; Antenna (radio); Signal-to-noise ratio (imaging); Algorithm; Spectrum (functional analysis); Statistics; Electronic engineering; Mathematics; Artificial intelligence; Telecommunications; Physics; Engineering","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":[0.000224856,0.0001779042,0.0002599978,0.0001833366,0.00009754043,0.0000862482,0.0001837284,0.00005954815,0.00001143241],"category_scores_gemma":[0.00004807668,0.0001477321,0.0000267043,0.0005794694,0.0002674444,0.0005376432,0.0001663595,0.0001456437,0.000006282871],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004212732,"about_ca_system_score_gemma":0.0001339676,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005728169,"about_ca_topic_score_gemma":0.001836372,"domain_scores_codex":[0.9985527,0.00008999932,0.0003117303,0.0004602426,0.0002817445,0.0003035859],"domain_scores_gemma":[0.9990017,0.0003441471,0.0001493929,0.0002901623,0.000121593,0.00009293756],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.004843865,0.0008080433,0.7038632,0.0002132763,0.0004193588,0.001717734,0.01903296,0.01448204,0.08572327,0.007195021,0.0008672407,0.160834],"study_design_scores_gemma":[0.001434839,0.0003750816,0.1705726,0.0001819271,0.000008672981,0.00008355011,0.0000370992,0.8244958,0.001924415,0.000608824,0.00002026198,0.000256914],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7938479,0.00003330433,0.2014604,0.0005904982,0.00007192801,0.0002953768,0.000002549189,0.00006842951,0.003629626],"genre_scores_gemma":[0.9362096,0.00001246216,0.06345841,0.0001251515,0.00005266332,5.457775e-7,0.000007978683,0.0000137099,0.0001194912],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8100138,"threshold_uncertainty_score":0.602434,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007199821415860444,"score_gpt":0.2109605487480654,"score_spread":0.2037607273322049,"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."}}