{"id":"W2380763832","doi":"","title":"An Improved SLM for PAPR Reduction of OFDM","year":2010,"lang":"en","type":"article","venue":"Microcomputer applications","topic":"PAPR reduction in OFDM","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Orthogonal frequency-division multiplexing; Computer science; Transmitter; Bit error rate; Reduction (mathematics); Bandwidth (computing); Multiplexing; Algorithm; Electronic engineering; SIGNAL (programming language); Real-time computing; Telecommunications; Channel (broadcasting); Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006600024,0.00009969516,0.0001075443,0.00008289738,0.00006427762,0.00002040831,0.0002101192,0.00008391481,0.00001237483],"category_scores_gemma":[3.74181e-7,0.0001136491,0.00005585295,0.0001456175,0.00004516727,0.0001110618,0.00001297285,0.0001240431,0.00001604377],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001837634,"about_ca_system_score_gemma":0.00001383621,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005810026,"about_ca_topic_score_gemma":0.000003429632,"domain_scores_codex":[0.9994164,0.000004256777,0.0002205753,0.000179395,0.00004101149,0.0001383667],"domain_scores_gemma":[0.9994337,0.00001535778,0.00004363853,0.0003547093,0.00009217657,0.00006041551],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000001717704,0.00004519889,0.00001099007,0.00003560564,0.00001437134,8.538612e-9,0.0001076836,0.001006459,0.9183527,0.00105487,0.001862951,0.0775075],"study_design_scores_gemma":[0.0002885558,0.00002818071,0.0004400809,0.000003650111,0.0000186606,0.00002170564,0.00002940196,0.02511116,0.5317099,0.001281321,0.4408827,0.0001847387],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1126141,0.0000299073,0.8851667,0.00008601916,0.0002347795,0.0009436324,0.00004555747,0.0004025695,0.0004767377],"genre_scores_gemma":[0.5704993,0.000004923465,0.4277151,0.00001272021,0.0006574124,0.0009426148,0.00006437188,0.00003649526,0.00006709941],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.4578852,"threshold_uncertainty_score":0.4634475,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00490122613886564,"score_gpt":0.2371356335223634,"score_spread":0.2322344073834978,"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."}}