{"id":"W2138552207","doi":"10.1109/tvt.2008.921617","title":"Performance Prediction for Energy Detection of Unknown Signals","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":50,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Energy (signal processing); Detection theory; Detector; Probability density function; Statistical power; Noise (video); Algorithm; Signal-to-noise ratio (imaging); Sliding window protocol; SIGNAL (programming language); Noise power; Computer science; Detection threshold; Power (physics); Mathematics; Statistics; Window (computing); Artificial intelligence; Physics; Telecommunications; Real-time computing","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":[0.003709178,0.001069736,0.0008992459,0.0007330383,0.0003929769,0.001255762,0.0009796867,0.001041753,0.001171235],"category_scores_gemma":[0.02675798,0.0003488779,0.0003127276,0.0004705563,0.001417697,0.001788517,0.001006928,0.001230076,0.0005648812],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001475461,"about_ca_system_score_gemma":0.0009617506,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003233436,"about_ca_topic_score_gemma":0.001459397,"domain_scores_codex":[0.9982356,0.000482905,0.00005475572,0.0003441523,0.0006020216,0.0002805645],"domain_scores_gemma":[0.9867821,0.01041188,0.0006861101,0.0006091206,0.001334085,0.0001766843],"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":[0.0003410713,0.00006197427,0.003940991,0.00008015907,0.00003636053,0.0001434164,0.0001291596,0.9251171,0.005467803,0.0227099,0.0008112698,0.04116078],"study_design_scores_gemma":[0.000003906975,0.00004578233,0.0005732324,0.00001029256,0.000005260945,0.0000393247,0.00001319462,0.9941655,0.001882024,0.003127636,0.000121991,0.00001171304],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2170376,0.001638982,0.7748358,0.000685158,0.0001120567,0.0000440981,0.0001226746,0.000770422,0.004753171],"genre_scores_gemma":[0.983414,0.0003741879,0.01490027,0.00005919186,0.00003742113,0.00002136666,0.00009474008,0.00005682613,0.001042009],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003709178,"threshold_uncertainty_score":0.01961625,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01131210062436753,"score_gpt":0.2023674513235285,"score_spread":0.191055350699161,"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."}}