{"id":"W3196800729","doi":"10.1109/tit.2021.3099020","title":"Error Floor Analysis of LDPC Row Layered Decoders","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Information Theory","topic":"Error Correcting Code Techniques","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Low-density parity-check code; Computer science; Algorithm; Scheduling (production processes); Flooding (psychology); Schedule; Error floor; Decoding methods; Mathematics; Mathematical optimization","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":[0.0005884725,0.0004782863,0.000366232,0.0004215046,0.0002013358,0.0004785508,0.0004523096,0.0003679149,0.0008990759],"category_scores_gemma":[0.003215075,0.0002271172,0.0002480666,0.0003427285,0.0006984031,0.0006962144,0.0005555779,0.0004802661,0.0001915133],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009698572,"about_ca_system_score_gemma":0.0008631184,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002783154,"about_ca_topic_score_gemma":0.002015268,"domain_scores_codex":[0.9995486,0.0001210183,0.00001576486,0.0000364835,0.0002109049,0.00006719596],"domain_scores_gemma":[0.9982576,0.001076744,0.0002009233,0.0001386691,0.0002800438,0.00004615444],"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":[0.00005077347,0.00001180231,0.0006909318,0.00003688258,0.00001791768,0.00006794363,0.0000400388,0.9659798,0.009256378,0.01926968,0.0001365655,0.004441305],"study_design_scores_gemma":[0.000001201759,0.00001134337,0.00009942632,0.000002665075,0.000002643947,0.00001292458,0.000005071161,0.9957114,0.001895888,0.002198272,0.00005586883,0.000003257959],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2533234,0.0003413379,0.7411231,0.0001393927,0.00001900878,0.00002659106,0.0001375406,0.0003505611,0.004539024],"genre_scores_gemma":[0.9695854,0.0001833851,0.02834678,0.00003157686,0.000007613856,0.00002247551,0.00007389212,0.00004296997,0.001705852],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002783154,"threshold_uncertainty_score":0.007036865,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01367181924589739,"score_gpt":0.2584304687743,"score_spread":0.2447586495284026,"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."}}