{"id":"W3046030874","doi":"10.48550/arxiv.2007.15647","title":"Fast Thresholded SC-Flip Decoding of Polar Codes","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Error Correcting Code Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Decoding methods; Computer science; Sequential decoding; Algorithm; List decoding; Code (set theory); Polar code; Error detection and correction; Set (abstract data type); Berlekamp–Welch algorithm; Soft-decision decoder; Concatenated error correction code; Block 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003338207,0.0003821552,0.0005594866,0.0003682855,0.0001331091,0.0001040871,0.003467539,0.0003260405,0.00001686078],"category_scores_gemma":[0.0001224236,0.0004706758,0.0003139515,0.0008675274,0.0001393969,0.0003756393,0.004254614,0.0008566314,0.00003551477],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001988037,"about_ca_system_score_gemma":0.0002545111,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003056081,"about_ca_topic_score_gemma":0.00008917804,"domain_scores_codex":[0.9977106,0.0001626623,0.0003211052,0.001264832,0.000160654,0.0003801296],"domain_scores_gemma":[0.9973452,0.0001735986,0.0004997787,0.001556902,0.0002355481,0.0001889543],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001464572,0.000429911,0.08223523,0.0007607721,0.0005750406,0.001480779,0.004298986,0.06178828,0.01006026,0.8280767,0.003353444,0.006794116],"study_design_scores_gemma":[0.000548956,0.0002127655,0.002866893,0.0005716469,0.0001422551,0.00001561522,0.0002105178,0.8419423,0.02334529,0.1285228,0.0003130452,0.001307903],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2326409,0.00006571379,0.7603738,0.0001636551,0.0005073845,0.0002887626,0.00001977977,0.001059056,0.004881005],"genre_scores_gemma":[0.9747207,0.00008584924,0.02477053,0.0001014382,0.00005999274,8.286711e-7,0.000008522799,0.0000289926,0.0002231595],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.780154,"threshold_uncertainty_score":0.9997745,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1071563631480257,"score_gpt":0.2140341889019091,"score_spread":0.1068778257538834,"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."}}