{"id":"W1972491259","doi":"10.1002/ett.1470","title":"Linear combination‐based energy detection algorithm in low signal‐to‐noise ratio for cognitive radios","year":2011,"lang":"en","type":"article","venue":"European Transactions on Telecommunications","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"National Natural Science Foundation of China","keywords":"Cognitive radio; False alarm; Energy (signal processing); Algorithm; Computer science; Detection theory; Statistical power; SIGNAL (programming language); Noise (video); Signal-to-noise ratio (imaging); Telecommunications; Artificial intelligence; Mathematics; Wireless; Statistics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001204916,0.0006654841,0.0008272404,0.0006876021,0.0004060003,0.0009204719,0.001060443,0.0006393498,0.001127229],"category_scores_gemma":[0.003488553,0.0004354807,0.0003625795,0.0007060643,0.000630711,0.001010007,0.0009896349,0.0008086393,0.0004598601],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005580609,"about_ca_system_score_gemma":0.0006799576,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008140256,"about_ca_topic_score_gemma":0.0009136961,"domain_scores_codex":[0.9984508,0.0004873818,0.00007381044,0.0002974113,0.00057927,0.0001113865],"domain_scores_gemma":[0.9985697,0.0007922538,0.0001749265,0.0001051555,0.0003052652,0.00005273143],"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":[0.001154873,0.0003268442,0.00204897,0.0001947969,0.0002108767,0.0002072503,0.0002382281,0.2580193,0.05526488,0.01301072,0.001933745,0.6673895],"study_design_scores_gemma":[0.00003682446,0.0001281253,0.0003151114,0.000008446063,0.00002796172,0.0001251247,0.00001170657,0.9867584,0.009408644,0.002644557,0.0005181841,0.00001683848],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02313859,0.0002147345,0.9750889,0.00008089429,0.00002857701,0.00003134165,0.000007587422,0.0003701543,0.001039329],"genre_scores_gemma":[0.6207276,0.0001445587,0.3768174,0.0001677901,0.0000645532,0.0001342825,0.00004164229,0.00004202039,0.001860155],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001204916,"threshold_uncertainty_score":0.006372333,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02399173461659212,"score_gpt":0.2369225866670555,"score_spread":0.2129308520504633,"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."}}