{"id":"W2116958184","doi":"10.1109/vetecf.2007.258","title":"BERT Chart Analysis of Adaptive and Non-Adaptive Turbo Frequency Domain Equalization","year":2007,"lang":"en","type":"article","venue":"IEEE Vehicular Technology Conference","topic":"Advanced Wireless Communication Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"EXIT chart; Turbo equalizer; Turbo; Computer science; Equalization (audio); Adaptive equalizer; Bit error rate; Chart; Algorithm; Channel (broadcasting); Equalizer; Frequency domain; QAM; Quadrature amplitude modulation; Decoding methods; Statistics; Mathematics; Telecommunications; Low-density parity-check code","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":[0.001252143,0.0005528153,0.0004636724,0.0006481036,0.0002855424,0.0006796534,0.0003972279,0.0004806872,0.001844657],"category_scores_gemma":[0.008274114,0.0001847492,0.000290256,0.0005428458,0.0006695885,0.001318186,0.0003260933,0.0004873761,0.0003089372],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008094226,"about_ca_system_score_gemma":0.000442773,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00216269,"about_ca_topic_score_gemma":0.00106811,"domain_scores_codex":[0.9991441,0.0002335802,0.00002318483,0.00005930637,0.0004374273,0.0001023102],"domain_scores_gemma":[0.9958598,0.002570566,0.0003335946,0.0002741702,0.0008964104,0.00006542464],"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":[0.0004620223,0.00003855026,0.002235749,0.00008076872,0.00005307935,0.0002513095,0.00008731332,0.896277,0.01976256,0.03513552,0.001048288,0.04456784],"study_design_scores_gemma":[0.000003186123,0.00003244556,0.0007534755,0.000004437517,0.000005703207,0.00006541057,0.000004640319,0.9929431,0.00411148,0.001821425,0.0002415069,0.00001311714],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2116736,0.001262077,0.7748385,0.0001662709,0.00007370166,0.00004855876,0.0001044471,0.001147614,0.01068517],"genre_scores_gemma":[0.9737345,0.0003475898,0.02384012,0.00002989117,0.00003491307,0.00001904803,0.00007134557,0.00007984461,0.001842765],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00216269,"threshold_uncertainty_score":0.006622076,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0138420288300373,"score_gpt":0.2493511695216451,"score_spread":0.2355091406916078,"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."}}