{"id":"W2625124615","doi":"10.1109/jlt.2017.2716373","title":"Low-Complexity Soft-Decision Concatenated LDGM-Staircase FEC for High-Bit-Rate Fiber-Optic Communication","year":2017,"lang":"en","type":"article","venue":"Journal of Lightwave Technology","topic":"Error Correcting Code Techniques","field":"Computer Science","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Coding gain; Concatenated error correction code; Decoding methods; Constant-weight code; Forward error correction; Computer science; Algorithm; Systematic code; Bit error rate; Code rate; Computational complexity theory; Code (set theory); Cyclic code; Theoretical computer science; Block code","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00036639,0.0005005088,0.0002964253,0.0004602171,0.0004163028,0.0004556381,0.000684385,0.0005216151,0.0008241007],"category_scores_gemma":[0.0013793,0.0001680617,0.0002311811,0.0004999916,0.0003762987,0.0005248014,0.0004572015,0.0005197057,0.0002815816],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008735013,"about_ca_system_score_gemma":0.0009534091,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002301126,"about_ca_topic_score_gemma":0.007333212,"domain_scores_codex":[0.9994922,0.00009777308,0.0000236338,0.00005591669,0.0002808392,0.0000496974],"domain_scores_gemma":[0.9990579,0.0003213574,0.0001614568,0.0001372043,0.0002820737,0.000039987],"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":[0.0005726143,0.0002056287,0.002175138,0.0002122806,0.00009316251,0.0005753172,0.0002430663,0.4859775,0.2686401,0.02387669,0.001249231,0.2161793],"study_design_scores_gemma":[0.00001984604,0.0002154957,0.0004811211,0.00001929956,0.00002393717,0.0002659705,0.00001298047,0.9210142,0.07349275,0.002744825,0.001681791,0.00002772843],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1615317,0.0004812934,0.832841,0.0002460546,0.00005240263,0.00009848215,0.00009596111,0.0005959853,0.004056999],"genre_scores_gemma":[0.7964816,0.0001442621,0.2008507,0.00006430198,0.00001642883,0.00004304832,0.00008465872,0.00001691172,0.00229815],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002301126,"threshold_uncertainty_score":0.006337762,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03422018111336399,"score_gpt":0.3131512780376215,"score_spread":0.2789310969242575,"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."}}