{"id":"W2111789128","doi":"10.1109/isit.2002.1023726","title":"Designing irregular LPDC codes using EXIT charts based on message error rate","year":2003,"lang":"en","type":"article","venue":"","topic":"Error Correcting Code Techniques","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Low-density parity-check code; Computer science; Additive white Gaussian noise; Algorithm; Turbo code; Decoding methods; Information transfer; Gaussian; Word error rate; Range (aeronautics); Theoretical computer science; Channel (broadcasting); Telecommunications; Speech recognition; 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":[0.001258523,0.0007989644,0.0006269057,0.001037011,0.0004756523,0.0009808929,0.0005882154,0.0004291029,0.0009293247],"category_scores_gemma":[0.005415117,0.0003045988,0.0003935784,0.0007683102,0.001270808,0.001033633,0.0008235258,0.0009754564,0.0003147379],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00098467,"about_ca_system_score_gemma":0.0006999159,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009967895,"about_ca_topic_score_gemma":0.0006924761,"domain_scores_codex":[0.9991457,0.000282688,0.00003927627,0.00007607785,0.0003617749,0.0000944717],"domain_scores_gemma":[0.9969432,0.001576382,0.0003663914,0.0004681979,0.0005530947,0.00009277707],"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":[0.0001444607,0.00002700829,0.001150847,0.00007788881,0.00002220443,0.000155044,0.0001678842,0.8443294,0.01678575,0.1025656,0.0006383976,0.03393553],"study_design_scores_gemma":[0.000007027202,0.00003784384,0.0001068866,0.000009225409,0.000006023269,0.00004324211,0.00001272682,0.9760015,0.008124883,0.01464703,0.0009900881,0.00001354192],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0364562,0.0001272252,0.9606371,0.00006135038,0.00002011079,0.0000327651,0.00003543651,0.0004719635,0.002157902],"genre_scores_gemma":[0.7685806,0.0004862375,0.2280201,0.00007784171,0.00004609388,0.0001696201,0.0001698704,0.0002662441,0.00218337],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001258523,"threshold_uncertainty_score":0.007144332,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0523591337626671,"score_gpt":0.2917638333100579,"score_spread":0.2394046995473908,"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."}}