{"id":"W2107283447","doi":"10.1109/lcomm.2005.11014","title":"Distance-based-decoding of block turbo codes","year":2005,"lang":"en","type":"article","venue":"IEEE Communications Letters","topic":"PAPR reduction in OFDM","field":"Engineering","cited_by":107,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University; University of Alberta","funders":"","keywords":"Orthogonal frequency-division multiplexing; Reduction (mathematics); Algorithm; Decoding methods; Sequence (biology); Computational complexity theory; Computation; Computer science; Mathematics; Multiplexing; Mathematical optimization; Telecommunications; Channel (broadcasting)","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":[0.00009257376,0.0001000843,0.0001247283,0.0001081524,0.0000906253,0.00001634323,0.0006575882,0.00003507753,0.00002303197],"category_scores_gemma":[0.00001503613,0.0001165248,0.00006024265,0.0001970473,0.0001629395,0.0001105083,0.00002938684,0.0001785906,0.00004097413],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001152575,"about_ca_system_score_gemma":0.00001008975,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008336845,"about_ca_topic_score_gemma":0.00002612982,"domain_scores_codex":[0.999377,0.00003756666,0.0002581497,0.00008079982,0.0001032894,0.0001432548],"domain_scores_gemma":[0.9984635,0.0001345946,0.00005054293,0.001280773,0.00003154754,0.00003906567],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000005211985,0.0000963704,0.001369309,0.00007287675,0.00009260734,5.473566e-7,0.0005820012,0.5500737,0.3891013,0.0007506682,0.04777648,0.01007894],"study_design_scores_gemma":[0.0006444433,0.00001505499,0.001216251,0.0002039289,0.00006576415,0.00001337229,0.000136687,0.2739728,0.3357605,0.00006318352,0.3872819,0.000626168],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9169486,0.002633636,0.03833219,0.02433765,0.0008322826,0.000354929,0.00005818941,0.001021243,0.01548134],"genre_scores_gemma":[0.9747672,0.000132216,0.02458115,0.0003438991,0.00007540917,0.00003482548,0.000009944042,0.00002448274,0.00003089162],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3395054,"threshold_uncertainty_score":0.4751743,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02422769666727629,"score_gpt":0.2555217081489631,"score_spread":0.2312940114816869,"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."}}