{"id":"W2254538408","doi":"10.1109/tsp.2015.2505685","title":"Scaled Largest Eigenvalue Detection for Stationary Time-Series","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Signal Processing","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"False alarm; Detector; Robustness (evolution); Algorithm; Test statistic; Mathematics; Covariance matrix; Eigenvalues and eigenvectors; Statistic; Statistics; Noise (video); Computer science; Statistical hypothesis testing; Artificial intelligence; Telecommunications","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002955786,0.0001594354,0.0001555803,0.0001530016,0.0005158627,0.0002381159,0.000161462,0.00007025823,0.00001027272],"category_scores_gemma":[0.000005787811,0.0001601992,0.00008195936,0.0004206502,0.00005007104,0.0009905969,0.000001579807,0.0001612707,0.00003174473],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001073309,"about_ca_system_score_gemma":0.0001747353,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006367433,"about_ca_topic_score_gemma":0.00002047952,"domain_scores_codex":[0.9988158,0.00005811588,0.0002157073,0.0003568727,0.0002735574,0.0002799131],"domain_scores_gemma":[0.9992612,0.0001125783,0.00008060077,0.0001317587,0.0002810429,0.0001328168],"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":[0.0001934529,0.0001314212,0.000002932105,0.00002793969,0.00002612918,0.000008774417,0.0006027845,0.03561877,0.008147029,0.0001005488,0.00008944943,0.9550508],"study_design_scores_gemma":[0.0007742906,0.0004196218,0.00002982967,0.00007709768,0.00002919946,0.00007602813,0.00009113512,0.9007645,0.09318215,0.003429542,0.000829535,0.0002970944],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003910377,0.0001216799,0.994506,0.0003031665,0.0002633967,0.0002364931,0.000007648498,0.0002530731,0.0003981826],"genre_scores_gemma":[0.9677095,0.000004675716,0.03154129,0.0001487262,0.0001472959,0.00003314275,0.000002478969,0.00002086921,0.0003919685],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9637992,"threshold_uncertainty_score":0.6532732,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0246130719963134,"score_gpt":0.2525728340881724,"score_spread":0.227959762091859,"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."}}