{"id":"W4285292416","doi":"10.1109/access.2022.3188675","title":"Novel PAPR Reduction Algorithms for OFDM Signals","year":2022,"lang":"en","type":"article","venue":"IEEE Access","topic":"PAPR reduction in OFDM","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Orthogonal frequency-division multiplexing; Algorithm; Reduction (mathematics); Computer science; Clipping (morphology); SIGNAL (programming language); Time domain; Frequency domain; Channel (broadcasting); Mathematics; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001729121,0.0001233202,0.0001344743,0.0001279597,0.0002047854,0.00006674021,0.000363907,0.00003934789,0.0003099788],"category_scores_gemma":[0.00001290575,0.0001474421,0.00007395409,0.0003001371,0.00002105413,0.0003444687,0.00004962715,0.0001686338,0.00001439373],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001617676,"about_ca_system_score_gemma":0.00002090602,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002580673,"about_ca_topic_score_gemma":7.184416e-7,"domain_scores_codex":[0.999121,0.0000137829,0.0002121993,0.000204978,0.0002090041,0.0002390484],"domain_scores_gemma":[0.9995997,0.0000344978,0.00004474216,0.000221276,0.00004485234,0.00005492996],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001760889,0.00006130961,0.00001991173,0.00005769924,0.00007005927,0.000001454433,0.0001996599,0.6333561,0.2818439,0.0001145746,0.07286959,0.01138817],"study_design_scores_gemma":[0.001760687,0.0001441617,0.0003532517,0.00002148586,0.00007738193,0.0002602286,0.0005440696,0.1624368,0.5145506,0.002604507,0.3163153,0.0009315897],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4298615,0.0007836429,0.5226374,0.0008884872,0.03394489,0.001803123,0.00048571,0.002208803,0.007386386],"genre_scores_gemma":[0.9938892,0.00001838305,0.002777218,0.00006730485,0.001155342,0.0009060232,0.00003065506,0.00007331641,0.001082536],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5640277,"threshold_uncertainty_score":0.6012516,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06602072461479402,"score_gpt":0.3202665978642618,"score_spread":0.2542458732494677,"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."}}