{"id":"W2142155648","doi":"10.1109/vtcf.2006.421","title":"BER Transfer Chart Analysis of Turbo Frequency Domain Equalization","year":2006,"lang":"en","type":"article","venue":"IEEE Vehicular Technology Conference","topic":"Advanced Wireless Communication Techniques","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Turbo equalizer; EXIT chart; Bit error rate; Equalization (audio); Turbo; Computer science; Transfer function; Equalizer; Algorithm; Turbo code; Frequency domain; Chart; Decoding methods; Mathematics; Channel (broadcasting); Statistics; Telecommunications; Low-density parity-check code; 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":[0.001015589,0.0005118596,0.0005273449,0.001311181,0.0003019891,0.0006259698,0.0003005757,0.0007339547,0.002155733],"category_scores_gemma":[0.007278148,0.0001884989,0.000347435,0.0005770684,0.000625147,0.001339125,0.0003243681,0.0005884391,0.0007367142],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008490175,"about_ca_system_score_gemma":0.0003856823,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001783173,"about_ca_topic_score_gemma":0.0004888466,"domain_scores_codex":[0.9990923,0.0001915431,0.00002182125,0.00007557477,0.0005237125,0.00009501708],"domain_scores_gemma":[0.9973416,0.001368981,0.0002666806,0.0002471864,0.0007394086,0.0000362018],"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":[0.0004123613,0.00004805918,0.002267959,0.0000861452,0.00005850306,0.0003172681,0.0001421782,0.8907085,0.03268244,0.01570364,0.001158845,0.05641413],"study_design_scores_gemma":[0.000003725252,0.00006492716,0.001422959,0.000009140157,0.000009012279,0.0001158017,0.000008862324,0.9844493,0.01188294,0.001512526,0.0005012098,0.00001956877],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2518817,0.001202376,0.7283027,0.000212694,0.00008272006,0.00007197612,0.0001810764,0.003221306,0.01484351],"genre_scores_gemma":[0.9707727,0.0004522132,0.02519645,0.00005253096,0.00003154605,0.00003800092,0.0001452587,0.0002108047,0.003100347],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002155733,"threshold_uncertainty_score":0.007211626,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009123368468178067,"score_gpt":0.2254311194760778,"score_spread":0.2163077510078997,"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."}}