{"id":"W2110085878","doi":"10.1109/vetecs.2007.365","title":"PAPR Reduction in Wavelet Packet Modulation Using Tree Pruning","year":2007,"lang":"en","type":"article","venue":"","topic":"PAPR reduction in OFDM","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Orthogonal frequency-division multiplexing; Network packet; Wavelet packet decomposition; Tree (set theory); Reduction (mathematics); Wavelet; Redundancy (engineering); Modulation (music); Algorithm; Computer science; Wavelet transform; Mathematics; Pruning; Real-time computing; Telecommunications; Channel (broadcasting); Artificial intelligence; Computer network","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":[0.0002717065,0.00009607027,0.00008972015,0.0002396545,0.000033027,0.00001644591,0.00004329114,0.00008045054,0.0000666542],"category_scores_gemma":[0.0000173646,0.0001077232,0.0000236664,0.0003439632,0.00001526855,0.0002628412,0.000008865856,0.000126593,0.00001756075],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002485582,"about_ca_system_score_gemma":0.000007078389,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004902013,"about_ca_topic_score_gemma":0.00004004076,"domain_scores_codex":[0.9992855,0.00001130794,0.0002411067,0.0001232284,0.0001174942,0.0002213529],"domain_scores_gemma":[0.9997694,0.00001317845,0.00002280973,0.0001320981,0.00002033126,0.00004216008],"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":[0.00001251929,0.00002336762,0.001637129,0.0000316013,0.00001718404,0.000006575006,0.0007178586,0.4576859,0.4613334,0.001079486,0.000521848,0.07693317],"study_design_scores_gemma":[0.0005463814,0.0000163398,0.05675769,0.00005758675,0.000009041626,0.0001008372,0.0009804225,0.8188514,0.1197048,0.0009614286,0.001614051,0.000399964],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9187393,0.00002891141,0.05730896,0.0000253313,0.0006998251,0.0001013197,3.904212e-7,0.000303369,0.02279255],"genre_scores_gemma":[0.986513,0.000006789998,0.01298008,0.000005114475,0.0002362846,0.000002291319,0.00000540955,0.00002541961,0.0002255603],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3611656,"threshold_uncertainty_score":0.4392826,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02365767500796156,"score_gpt":0.2568929512668484,"score_spread":0.2332352762588868,"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."}}