{"id":"W2798867000","doi":"10.1109/glocom.2018.8648105","title":"Generalized Fast Decoding of Polar Codes","year":2018,"lang":"en","type":"preprint","venue":"","topic":"Error Correcting Code Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Decoding methods; List decoding; Sequential decoding; Computer science; Algorithm; Polar code; Berlekamp–Welch algorithm; Polar; Code (set theory); Coding (social sciences); Concatenated error correction code; Mathematics; Set (abstract data type); Block code; Statistics","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":[0.0006475796,0.0002689455,0.0004517408,0.0002629789,0.00006498877,0.0001387984,0.002527135,0.0002676434,0.00005491532],"category_scores_gemma":[0.0001231381,0.0002511623,0.0001854302,0.0002090418,0.00008213421,0.0001450581,0.003821205,0.0003611909,0.0000236877],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007431009,"about_ca_system_score_gemma":0.0001634251,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007328058,"about_ca_topic_score_gemma":0.0001117873,"domain_scores_codex":[0.9981349,0.0001243009,0.0004581719,0.0006642577,0.000337267,0.0002811209],"domain_scores_gemma":[0.9975581,0.00009709565,0.0003796929,0.001590989,0.0003018102,0.00007227639],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00006759348,0.0007497352,0.04261227,0.001865939,0.0008036069,0.00008773741,0.01269737,0.0007133806,0.09247125,0.5229115,0.1331335,0.1918861],"study_design_scores_gemma":[0.0003822815,0.000252488,0.001182267,0.0008090723,0.00004383133,0.00004663711,0.00004613608,0.2991703,0.5838934,0.1098238,0.002805851,0.00154394],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06194292,0.0001332322,0.9213376,0.0001922079,0.001160967,0.0002389972,0.000006780323,0.001403059,0.01358424],"genre_scores_gemma":[0.3702951,0.00003070579,0.6289893,0.0001170192,0.0001011841,0.00001579073,0.000004205413,0.00001715121,0.0004295361],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4914221,"threshold_uncertainty_score":0.999994,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03681981122579413,"score_gpt":0.3066811974545411,"score_spread":0.269861386228747,"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."}}