{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005482463,0.0001310567,0.0002298046,0.0007879968,0.000141413,0.00008020951,0.0004314343,0.00009907874,0.000219171],"category_scores_gemma":[0.00003745807,0.000135881,0.0002348912,0.001978931,0.00004201403,0.001496168,0.000004366284,0.0002058769,0.00007721913],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006808153,"about_ca_system_score_gemma":0.0001183708,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002354851,"about_ca_topic_score_gemma":0.00005479793,"domain_scores_codex":[0.9987341,0.0001580039,0.0004723435,0.0001607476,0.0003039114,0.0001708852],"domain_scores_gemma":[0.9985656,0.0002161424,0.0002029155,0.0006832607,0.0002704138,0.00006162975],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001437661,0.0004319731,0.00004434005,0.0001137455,0.001964927,0.00000783393,0.01951876,0.4457825,0.003482193,0.05367753,0.000660861,0.4741715],"study_design_scores_gemma":[0.0005100912,0.0001292498,0.0005074906,0.00005159818,0.0004462912,0.00002080182,0.001567547,0.5687535,0.4213872,0.005119268,0.001063261,0.0004437854],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009942371,0.00001140082,0.9855671,0.0001548559,0.0004492201,0.0001005841,0.0000275098,0.0004359764,0.003310972],"genre_scores_gemma":[0.9709573,0.00001327483,0.0282606,0.0005068197,0.000004862811,0.00002977944,0.00001053,0.000006016188,0.0002108606],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9610149,"threshold_uncertainty_score":0.5541067,"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."}}