{"id":"W1633543472","doi":"10.1109/lcomm.2015.2466105","title":"Approximations for Performance of Energy Detector and <inline-formula> <tex-math notation=\"LaTeX\">$p$</tex-math></inline-formula>-Norm Detector","year":2015,"lang":"en","type":"article","venue":"IEEE Communications Letters","topic":"Distributed Sensor Networks and Detection Algorithms","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Detector; Notation; Norm (philosophy); Mathematics; Gaussian; Energy (signal processing); Algorithm; Discrete mathematics; Applied mathematics; Combinatorics; Computer science; Statistics; Physics; Arithmetic; Quantum mechanics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006037289,0.001883024,0.001290862,0.001276289,0.001015927,0.002767814,0.002361482,0.002669146,0.008280878],"category_scores_gemma":[0.03821497,0.0005296054,0.00110832,0.001734289,0.001724449,0.003668423,0.002608409,0.00320643,0.005742332],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002659492,"about_ca_system_score_gemma":0.003091215,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006708027,"about_ca_topic_score_gemma":0.004720885,"domain_scores_codex":[0.9949567,0.001742035,0.0002124105,0.0007730616,0.001719327,0.0005964171],"domain_scores_gemma":[0.985076,0.01050244,0.0004679074,0.002050556,0.001659513,0.0002434545],"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":[0.001597664,0.0001787212,0.004259252,0.000858455,0.0001908675,0.000410687,0.0005891078,0.5061235,0.01815191,0.1510645,0.02040901,0.2961664],"study_design_scores_gemma":[0.00002051715,0.00006241735,0.0005560202,0.00007772582,0.00002466279,0.0003656212,0.00009468171,0.9602941,0.01022788,0.0245793,0.003645508,0.00005148551],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01447804,0.002548329,0.96905,0.0008855181,0.0002149829,0.00007790033,0.00038757,0.002837564,0.009520054],"genre_scores_gemma":[0.4155119,0.003094429,0.5677578,0.001021878,0.0002006877,0.0004052301,0.002164683,0.001256103,0.008587242],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008280878,"threshold_uncertainty_score":0.03192866,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02857176731792208,"score_gpt":0.2509157894916799,"score_spread":0.2223440221737578,"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."}}