{"id":"W2136318607","doi":"10.1109/newcas.2005.1496661","title":"Implementation and Error Performance Evaluation-of an Iterative Decoding Algorithm","year":2005,"lang":"en","type":"article","venue":"","topic":"Error Correcting Code Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Acceleration; Decoding methods; Algorithm; Noise (video); Error detection and correction; Signal-to-noise ratio (imaging); Task (project management); Range (aeronautics); Sensitivity (control systems); SIGNAL (programming language); Computer engineering; Electronic engineering; Artificial intelligence; Engineering; Telecommunications","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":[0.001012691,0.0005955492,0.0005164581,0.0006109687,0.0004050576,0.0007997398,0.0008858124,0.0009617116,0.002721545],"category_scores_gemma":[0.006211256,0.000190849,0.0002535293,0.0005072349,0.0003109915,0.001019169,0.0004631988,0.0006324068,0.001267151],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006516289,"about_ca_system_score_gemma":0.001128369,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002212229,"about_ca_topic_score_gemma":0.001560435,"domain_scores_codex":[0.9984313,0.0002502622,0.0001042097,0.0001903577,0.0009010474,0.0001227958],"domain_scores_gemma":[0.99657,0.001024811,0.0001933886,0.0006673438,0.001474956,0.00006953212],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001414139,0.0004155248,0.004194448,0.000309508,0.0001390034,0.000318228,0.0003769166,0.0786012,0.5515381,0.01125391,0.001768486,0.3496706],"study_design_scores_gemma":[0.00005619483,0.0008253439,0.00163513,0.00001674545,0.00003836934,0.0004918484,0.00003940208,0.3228808,0.6685641,0.0007266803,0.004684765,0.00004062013],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3079772,0.0003214871,0.679656,0.0001718552,0.0001123911,0.0002081588,0.0001923853,0.00417674,0.00718384],"genre_scores_gemma":[0.6265123,0.0001387438,0.3676504,0.00006224743,0.00001824164,0.0001160487,0.0003499639,0.0002726191,0.004879436],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002721545,"threshold_uncertainty_score":0.00910449,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0468232313682008,"score_gpt":0.3807374726851813,"score_spread":0.3339142413169806,"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."}}