{"id":"W4309757106","doi":"10.1109/tcomm.2022.3211101","title":"Decoding Reed-Muller Codes With Successive Codeword Permutations","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Communications","topic":"Error Correcting Code Techniques","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Code word; Decoding methods; Permutation (music); Algorithm; List decoding; Error detection and correction; Hadamard transform; Computational complexity theory; Computer science; Sequential decoding; Mathematics; Discrete mathematics; Arithmetic; Block code; Concatenated error correction code","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":["sts"],"consensus_categories":[],"category_scores_codex":[0.0003652791,0.0002038879,0.0001934253,0.0004142696,0.003257642,0.0001597469,0.00321924,0.00004578666,0.0001074934],"category_scores_gemma":[0.00001615328,0.0002150252,0.0001063096,0.001398754,0.0001709123,0.0004979598,0.00006369162,0.0009373993,0.00003131869],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002913418,"about_ca_system_score_gemma":0.0002080774,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002264319,"about_ca_topic_score_gemma":0.001055265,"domain_scores_codex":[0.998127,0.0004260263,0.0003229025,0.0004113442,0.000420451,0.0002922728],"domain_scores_gemma":[0.9954909,0.0010201,0.0001775183,0.003011394,0.0002018881,0.00009821419],"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":[0.0002779777,0.007313733,0.0008285734,0.00007368108,0.0009170122,0.00008436903,0.04388257,0.4180288,0.009768658,0.3037911,0.008164559,0.206869],"study_design_scores_gemma":[0.001705123,0.001574791,0.0004148209,0.0001916936,0.0002205856,0.0005068476,0.004845019,0.9332067,0.01911403,0.008362643,0.02794877,0.001908971],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002020927,0.00008077755,0.98503,0.006062477,0.0002622841,0.0004001228,0.00004925562,0.001221777,0.004872375],"genre_scores_gemma":[0.8726299,0.00006119256,0.1252811,0.0003999184,0.000007506599,0.0009439672,0.0000102145,0.00002957247,0.0006366236],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.870609,"threshold_uncertainty_score":0.99804,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03302514777196357,"score_gpt":0.2903340931662864,"score_spread":0.2573089453943228,"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."}}