{"id":"W3118220768","doi":"10.1109/jlt.2020.3046473","title":"Low-Complexity Rate- and Channel-Configurable Concatenated Codes","year":2020,"lang":"en","type":"article","venue":"Journal of Lightwave Technology","topic":"Error Correcting Code Techniques","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Huawei Technologies","keywords":"Concatenated error correction code; Decoding methods; Computer science; Forward error correction; Coding gain; Code rate; Low-density parity-check code; Error detection and correction; Channel (broadcasting); Serial concatenated convolutional codes; Bit error rate; Algorithm; Coding (social sciences); Computational complexity theory; Electronic engineering; Mathematics; Telecommunications; Block 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005119812,0.0001935688,0.0004889176,0.0004110265,0.0001052564,0.00008283451,0.001178782,0.0002265587,0.00001367413],"category_scores_gemma":[0.0004700899,0.0001617623,0.00007389272,0.0008555874,0.0002434725,0.0003941822,0.0003259915,0.0006605659,0.00001500462],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004818544,"about_ca_system_score_gemma":0.0000876497,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006525462,"about_ca_topic_score_gemma":0.000004374353,"domain_scores_codex":[0.9985477,0.00008999574,0.0005560812,0.000299566,0.0001892421,0.000317401],"domain_scores_gemma":[0.9984633,0.0001182525,0.0005777457,0.0003113836,0.0003706824,0.0001586385],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002280599,0.0003719165,0.003295987,0.0002496724,0.0003112957,0.002095967,0.00383609,0.0000447051,0.7196035,0.2177715,0.02025883,0.03193245],"study_design_scores_gemma":[0.0007807154,0.001388293,0.0005867105,0.0001207579,0.00002465039,0.00125058,0.0001777404,0.008104578,0.9121783,0.07146163,0.003584732,0.0003413093],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5156669,0.000995697,0.3971539,0.08210707,0.0006321582,0.0003002355,0.000003414158,0.001652785,0.00148778],"genre_scores_gemma":[0.9709242,0.00009439152,0.02789086,0.0009765598,0.00007058393,0.000002931661,3.121838e-7,0.00001436038,0.00002578298],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4552573,"threshold_uncertainty_score":0.6596476,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03122433610419903,"score_gpt":0.2560607306562759,"score_spread":0.2248363945520769,"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."}}