{"id":"W3161348053","doi":"10.1109/icassp39728.2021.9414311","title":"Towards Practical Near-Maximum-Likelihood Decoding of Error-Correcting Codes: An Overview","year":2021,"lang":"en","type":"article","venue":"","topic":"Error Correcting Code Techniques","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Decoding methods; Computer science; List decoding; Sequential decoding; Algorithm; Code (set theory); Berlekamp–Welch algorithm; Limit (mathematics); Field (mathematics); Implementation; Block (permutation group theory); Noisy-channel coding theorem; Theoretical computer science; Computer engineering; Concatenated error correction code; Block code; Mathematics; Programming language","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001554575,0.001089824,0.0009218815,0.002131022,0.0003663752,0.002577028,0.001175741,0.002318837,0.002925665],"category_scores_gemma":[0.004807177,0.0008509735,0.0005523202,0.003057866,0.001276718,0.003256973,0.001294451,0.00286999,0.003202911],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009539066,"about_ca_system_score_gemma":0.001478469,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001025999,"about_ca_topic_score_gemma":0.0008332367,"domain_scores_codex":[0.9987009,0.0003790523,0.000103645,0.0001805444,0.0005829935,0.00005277826],"domain_scores_gemma":[0.9978774,0.001350269,0.00007629363,0.0001430732,0.0005123977,0.00004075406],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00009661519,0.00009829403,0.0005992373,0.003661627,0.00008225746,0.0002134958,0.0002208342,0.05160683,0.007282765,0.2760167,0.01277844,0.647343],"study_design_scores_gemma":[0.00004322576,0.0004429147,0.0007321556,0.001696147,0.00008160751,0.002461534,0.0001259702,0.3137265,0.01696746,0.2562767,0.4072867,0.0001591881],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.001915121,0.1080939,0.8744497,0.001287346,0.0003517182,0.00006833745,0.00008793593,0.000440399,0.01330551],"genre_scores_gemma":[0.04831627,0.3273114,0.6130705,0.0009548315,0.002302455,0.0002133417,0.0006296661,0.0003297363,0.006871788],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.002925665,"threshold_uncertainty_score":0.009787321,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1108923124351034,"score_gpt":0.3919479717937379,"score_spread":0.2810556593586345,"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."}}