{"id":"W1972981840","doi":"10.1109/iraniancee.2013.6599636","title":"A random matrix approach to wide band spectrum sensing: Unknown noise variance case","year":2013,"lang":"en","type":"preprint","venue":"","topic":"Distributed Sensor Networks and Detection Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Variance (accounting); Noise (video); Spectrum (functional analysis); Random matrix; Algorithm; Energy (signal processing); Statistics; Computer science; Random noise; Matrix (chemical analysis); Mathematics; SIGNAL (programming language); Distribution (mathematics); Artificial intelligence; Physics; Eigenvalues and eigenvectors; Mathematical analysis","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":[0.001752947,0.0008706386,0.0005975954,0.0007621577,0.0002848817,0.000860043,0.0009503998,0.001122928,0.001478987],"category_scores_gemma":[0.005630671,0.0002713519,0.0005741836,0.0006894772,0.001119034,0.001915594,0.0007945615,0.001047417,0.0003914678],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004740192,"about_ca_system_score_gemma":0.000438589,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008018474,"about_ca_topic_score_gemma":0.0007593391,"domain_scores_codex":[0.9981648,0.0007697584,0.00006831629,0.000332181,0.0005775383,0.00008737855],"domain_scores_gemma":[0.9968803,0.00233815,0.0002328452,0.0002349982,0.0002701562,0.00004364627],"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":[0.0001951115,0.00008764524,0.0009219742,0.0003868509,0.0001359863,0.0005366434,0.0002069037,0.6073031,0.0174257,0.2945843,0.0008995227,0.0773162],"study_design_scores_gemma":[0.00001177143,0.00005573836,0.0001979864,0.00001626646,0.00001623795,0.0003068825,0.00002650248,0.9662955,0.002625692,0.02930919,0.001117756,0.00002043166],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003999945,0.0002345675,0.9946588,0.00007586972,0.00001863718,0.00001720719,0.00001099058,0.00003499737,0.000949003],"genre_scores_gemma":[0.5712713,0.001816342,0.4215482,0.0002416377,0.000245011,0.0001634853,0.0001020567,0.00006371252,0.004548295],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001752947,"threshold_uncertainty_score":0.009270549,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01422697322688897,"score_gpt":0.2361861073083723,"score_spread":0.2219591340814834,"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."}}