{"id":"W2120955921","doi":"10.1007/s10470-007-9038-8","title":"An efficient crest factor reduction technique for wideband applications","year":2007,"lang":"en","type":"article","venue":"Analog Integrated Circuits and Signal Processing","topic":"PAPR reduction in OFDM","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":false,"ca_institutions":"Simon Fraser University","funders":"Simon Fraser University; Kwangwoon University","keywords":"Crest factor; Reduction (mathematics); Wideband; Amplifier; Computer science; Electronic engineering; Power (physics); Algorithm; Mathematics; Bandwidth (computing); Engineering; Telecommunications; Physics","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":[0.0001220394,0.0004894522,0.0003176662,0.0003963088,0.0002929072,0.0003673874,0.000400188,0.0003682837,0.002504335],"category_scores_gemma":[0.0005272829,0.0002040346,0.0003152716,0.0005540641,0.0001825676,0.00038265,0.0003123742,0.000544288,0.001143079],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000150539,"about_ca_system_score_gemma":0.0002198295,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003715668,"about_ca_topic_score_gemma":0.001270861,"domain_scores_codex":[0.9997929,0.00002853357,0.000007677121,0.00002194603,0.0001326372,0.00001643451],"domain_scores_gemma":[0.9997944,0.00006744771,0.0000216562,0.00003854403,0.00006935214,0.000008585847],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001926814,0.00007826115,0.000231887,0.0001215195,0.00003476383,0.000186405,0.0001286118,0.00643108,0.5946578,0.01144194,0.002487944,0.3840071],"study_design_scores_gemma":[0.00007183368,0.0006620554,0.002258786,0.00006261693,0.0001948003,0.003305638,0.00008104996,0.3996214,0.534901,0.006714416,0.05205508,0.00007128357],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02040286,0.0005522132,0.9733471,0.0001170286,0.0000591704,0.00002994172,0.00003297181,0.0006033079,0.004855407],"genre_scores_gemma":[0.2587492,0.0008482927,0.7251298,0.0001992083,0.0001658063,0.00006257104,0.000142396,0.0001580702,0.01454466],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002504335,"threshold_uncertainty_score":0.00837791,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01563949387286772,"score_gpt":0.2658787501246501,"score_spread":0.2502392562517823,"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."}}