{"id":"W2058261656","doi":"10.4236/ijcns.2010.39101","title":"A New Effective and Efficient Measure of PAPR in OFDM","year":2010,"lang":"en","type":"article","venue":"International Journal of Communications Network and System Sciences","topic":"PAPR reduction in OFDM","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Measure (data warehouse); Orthogonal frequency-division multiplexing; Variance (accounting); Inefficiency; SIGNAL (programming language); Autocorrelation; Computer science; Power (physics); Aperiodic graph; Wireless; Mathematics; Electronic engineering; Telecommunications; Statistics; Channel (broadcasting); Physics; Data mining; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000936077,0.00005421382,0.0001307024,0.0001709645,0.00005774519,0.00004305307,0.0005487945,0.00003198756,0.000003150909],"category_scores_gemma":[0.00004593812,0.00004483225,0.00002646348,0.0002032131,0.0001785291,0.0001087318,0.00006871841,0.0001892912,4.921848e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002686266,"about_ca_system_score_gemma":0.00004482109,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002651148,"about_ca_topic_score_gemma":0.00005879983,"domain_scores_codex":[0.9992304,0.0000630413,0.0003341502,0.00005055935,0.0002516035,0.00007024369],"domain_scores_gemma":[0.9992622,0.00024411,0.0001508217,0.0001382606,0.0001554591,0.00004917273],"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":[0.00008813609,0.0001564688,0.1095809,0.0001223135,0.0004306281,0.00001117045,0.006991841,0.4614049,0.01689387,0.2019804,0.00211499,0.2002243],"study_design_scores_gemma":[0.003898078,0.0004400173,0.3516728,0.004172591,0.0001100648,0.003148149,0.007302983,0.6030498,0.002126361,0.005839588,0.01749348,0.0007461224],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9797254,0.005378748,0.004004677,0.0006472818,0.001881754,0.0001276903,0.000002262885,0.00001732157,0.00821484],"genre_scores_gemma":[0.9939266,0.0002047954,0.005718267,0.000003984459,0.0001350907,0.000002615294,1.939815e-7,0.000003033048,0.000005456926],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2420919,"threshold_uncertainty_score":0.1828206,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01142483566407352,"score_gpt":0.2681614884771797,"score_spread":0.2567366528131062,"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."}}