{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002617782,0.000208263,0.0004292683,0.001107319,0.00006036946,0.00000838511,0.0003849279,0.0003686357,0.00001085818],"category_scores_gemma":[0.00003097161,0.000225032,0.00006326754,0.001711626,0.0003656123,0.0001479152,0.00006801711,0.0003181327,0.000003379022],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000765821,"about_ca_system_score_gemma":0.00002093127,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003287437,"about_ca_topic_score_gemma":0.0001595909,"domain_scores_codex":[0.9988875,0.00002816858,0.0004098805,0.0002634499,0.0001504878,0.0002604776],"domain_scores_gemma":[0.9988236,0.00007862451,0.0001456302,0.0006520054,0.0002515918,0.00004854005],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00003168465,0.00008938323,0.01477875,0.00005721078,0.001275068,0.00002981855,0.001134928,0.004038803,0.7157878,0.2304935,0.00001934572,0.03226375],"study_design_scores_gemma":[0.0004222974,0.0002160578,0.009339803,0.0001215418,0.0003647615,0.000009549897,0.001329003,0.08128107,0.8517129,0.05447259,0.00009080244,0.0006396426],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.417272,0.0003694176,0.5807886,0.00004475942,0.00002572603,0.0001682778,0.000009583114,0.000474279,0.0008472947],"genre_scores_gemma":[0.9396201,0.0003046531,0.05996451,0.00001642111,0.00000642323,0.00004259345,0.00001608157,0.00002344661,0.000005792762],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.522348,"threshold_uncertainty_score":0.917654,"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."}}