{"id":"W2127472448","doi":"10.1109/wcnc.2008.82","title":"BERT Chart Analysis of Turbo Frequency Domain Equalization with Imperfect Channel State Information","year":2008,"lang":"en","type":"article","venue":"","topic":"Advanced Wireless Communication Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"EXIT chart; Computer science; Turbo; Channel (broadcasting); Turbo equalizer; Bit error rate; Equalization (audio); Frequency domain; Turbo code; Algorithm; Chart; Information transfer; Decoding methods; Telecommunications; Mathematics; Low-density parity-check code; Statistics; Concatenated error correction 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":[0.001296622,0.000566772,0.0005422607,0.0007659449,0.0002763035,0.0008809302,0.0003582908,0.0005366463,0.002640855],"category_scores_gemma":[0.00969374,0.0001600315,0.0002981764,0.0004795659,0.0008436293,0.001386586,0.0003656653,0.000554257,0.0004559144],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000750423,"about_ca_system_score_gemma":0.0005661797,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002020191,"about_ca_topic_score_gemma":0.0007161911,"domain_scores_codex":[0.9993187,0.0001764152,0.00001696661,0.00004438299,0.0003565299,0.0000870254],"domain_scores_gemma":[0.9964073,0.00233975,0.0002822772,0.0002748218,0.0006588811,0.00003693108],"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":[0.0001402292,0.00001895084,0.0006667342,0.00005453984,0.00002742632,0.0001526248,0.00004620154,0.9483615,0.006614079,0.02400196,0.0004982302,0.01941755],"study_design_scores_gemma":[0.000001950332,0.00002731415,0.0002385112,0.000004859118,0.000005408386,0.00004441587,0.000004740987,0.9943112,0.003515227,0.001658327,0.0001801729,0.000007782166],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1286249,0.0008479168,0.8605727,0.0001403136,0.00006004425,0.00005726299,0.00009467309,0.0009730528,0.008629193],"genre_scores_gemma":[0.9654765,0.0005571869,0.03059069,0.00003744195,0.00003360983,0.00003711569,0.00008474948,0.00008577137,0.003096951],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002640855,"threshold_uncertainty_score":0.008834481,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00803651062128469,"score_gpt":0.2112753109219908,"score_spread":0.2032388003007061,"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."}}