{"id":"W2540600821","doi":"10.1109/icu.2005.1569955","title":"Enhancing Multiband OFDM Performance: Capacity-Approaching Codes and Bit Loading","year":2006,"lang":"en","type":"article","venue":"","topic":"Ultra-Wideband Communications Technology","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Orthogonal frequency-division multiplexing; Computer science; Turbo code; Electronic engineering; Bit error rate; Physical layer; Channel (broadcasting); Ultra-wideband; Transmission (telecommunications); Wireless; Multiplexing; Computer network; Telecommunications; Decoding methods; Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008769413,0.0006603518,0.0005000086,0.0004851059,0.0003026134,0.0008966235,0.000605733,0.0006839843,0.0009105062],"category_scores_gemma":[0.006067188,0.0001719799,0.0001729254,0.0005878597,0.001100759,0.001450245,0.0006940365,0.0005887884,0.0004164149],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004602761,"about_ca_system_score_gemma":0.0005411049,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003740455,"about_ca_topic_score_gemma":0.000379422,"domain_scores_codex":[0.9992369,0.0002813518,0.00003176738,0.00006462309,0.0002998475,0.00008544447],"domain_scores_gemma":[0.9969679,0.001599944,0.0004139712,0.0004613991,0.0004867791,0.00007012055],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005653554,0.0002243185,0.002276646,0.0002696481,0.00005361799,0.0003108218,0.0002752228,0.5696899,0.09046605,0.1397037,0.0007240919,0.1954406],"study_design_scores_gemma":[0.00001976507,0.0003045682,0.0005305572,0.0000388107,0.00002203587,0.0002226132,0.00004304664,0.9033474,0.07434062,0.01923113,0.001862,0.0000373965],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1757957,0.001048798,0.8123626,0.0003328088,0.00005772072,0.00005212624,0.00002778144,0.0004497277,0.009872661],"genre_scores_gemma":[0.8885381,0.0004524062,0.1094549,0.0000931686,0.00005719722,0.00004429758,0.0000223182,0.00004980587,0.001287868],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0009105062,"threshold_uncertainty_score":0.004637778,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00888966027668723,"score_gpt":0.1868252488864973,"score_spread":0.1779355886098101,"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."}}