{"id":"W2170735835","doi":"10.1109/pimrc.2007.4394563","title":"Improved PAPR Reduction for Wavelet Packet Modulation using Multi-Pass Tree Pruning","year":2007,"lang":"en","type":"article","venue":"","topic":"PAPR reduction in OFDM","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Orthogonal frequency-division multiplexing; Tree (set theory); Reduction (mathematics); Redundancy (engineering); Network packet; Wavelet packet decomposition; Computer science; Modulation (music); Wavelet; Transmission (telecommunications); Algorithm; Wavelet transform; Mathematics; Real-time computing; Telecommunications; Computer network; Channel (broadcasting); Artificial intelligence","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":[0.0003090159,0.0001557887,0.0001340301,0.0001585994,0.000106285,0.00003279471,0.00006522078,0.0001258291,0.00002614492],"category_scores_gemma":[0.0000433106,0.0001676347,0.00006728526,0.0001919885,0.00002165496,0.0003044382,0.00001173608,0.0001086282,0.000005430031],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002449736,"about_ca_system_score_gemma":0.00001187724,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003021391,"about_ca_topic_score_gemma":0.00002270615,"domain_scores_codex":[0.9990569,0.000009196042,0.0003084922,0.0001972793,0.000101507,0.000326603],"domain_scores_gemma":[0.9995761,0.00002931986,0.00005201703,0.0001913295,0.00007684567,0.00007440842],"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":[0.00001900261,0.00002011135,0.00005343894,0.00004485627,0.0000366021,4.075567e-7,0.0002303623,0.04142451,0.914934,0.0002347279,0.0002765716,0.04272541],"study_design_scores_gemma":[0.0005779684,0.00001895854,0.001980048,0.0000133777,0.00001617818,0.00002013114,0.0003201466,0.842689,0.1530656,0.0001075078,0.0009790425,0.0002120122],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3410889,0.00002082785,0.6558686,0.00002514201,0.001091835,0.0003252834,0.00000329413,0.0004519378,0.001124166],"genre_scores_gemma":[0.8135848,0.000003364921,0.1853816,0.000006751971,0.0004249612,0.00001634879,0.00001968774,0.00004954747,0.00051296],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8012645,"threshold_uncertainty_score":0.6835944,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03901485272515705,"score_gpt":0.2844993746214767,"score_spread":0.2454845218963197,"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."}}