{"id":"W1499185341","doi":"10.1109/ccece.2004.1347674","title":"Design of a high-speed (255,239) RS decoder using 0.18 μm CMOS","year":2004,"lang":"en","type":"article","venue":"","topic":"Coding theory and cryptography","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Computer science; Very-large-scale integration; Soft-decision decoder; CMOS; Decoding methods; Latency (audio); Parallel computing; Computer hardware; Electronic engineering; Algorithm; Embedded system; 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":[0.0001544707,0.0002818193,0.0002131811,0.0002944373,0.0002601562,0.0004189642,0.0007942027,0.0003228653,0.002022911],"category_scores_gemma":[0.0003182358,0.0001724418,0.000170103,0.0002052631,0.0001846314,0.0003010988,0.0001718204,0.0002291573,0.001026139],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004530764,"about_ca_system_score_gemma":0.0007035516,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001053603,"about_ca_topic_score_gemma":0.001510873,"domain_scores_codex":[0.9998871,0.00001189047,0.000009038874,0.00002603261,0.00004865203,0.00001727349],"domain_scores_gemma":[0.9998199,0.00003855406,0.00003161107,0.0000132796,0.0000842576,0.00001240979],"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":[0.0002288096,0.00009230033,0.002241244,0.0003249035,0.00008770671,0.0005304739,0.0001708752,0.0120914,0.7826029,0.01340065,0.004044939,0.1841837],"study_design_scores_gemma":[0.0001862465,0.00140039,0.002652729,0.00006110607,0.0001733577,0.002157095,0.00009684335,0.1171293,0.8067726,0.002771718,0.06654327,0.00005517799],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2326413,0.001463386,0.7356188,0.0006018268,0.0003006902,0.0005578287,0.0004522103,0.004705201,0.02365879],"genre_scores_gemma":[0.5611525,0.0005808719,0.4289361,0.0002167869,0.00007901542,0.0001481644,0.0002409772,0.00007916756,0.008566375],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002022911,"threshold_uncertainty_score":0.006767333,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05203159697977826,"score_gpt":0.2628484752812741,"score_spread":0.2108168783014958,"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."}}