{"id":"W2601496744","doi":"10.1109/tsp.2017.2740204","title":"Fast and Flexible Successive-Cancellation List Decoders for Polar Codes","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Signal Processing","topic":"Error Correcting Code Techniques","field":"Computer Science","cited_by":207,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Decoding methods; Computer science; Algorithm; Error detection and correction; Coding gain; Polar code; Throughput; Polar; Coding (social sciences); Bit error rate; Parity bit; Computer engineering; Telecommunications; Mathematics; Statistics; Physics; Wireless","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":[0.0002840225,0.0001794527,0.0001770437,0.0001534265,0.001965638,0.0009702903,0.0005994313,0.00009123782,0.00000538729],"category_scores_gemma":[0.0000145609,0.0001830124,0.00006461214,0.0001165224,0.0001287181,0.001455163,0.000005639498,0.000200618,0.000002515088],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006316484,"about_ca_system_score_gemma":0.000110035,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001207008,"about_ca_topic_score_gemma":0.0001494912,"domain_scores_codex":[0.9988372,0.00002816796,0.0002096783,0.0004537625,0.0002024632,0.0002687106],"domain_scores_gemma":[0.9989699,0.0001506878,0.0002409659,0.000379886,0.0001723599,0.00008621555],"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":[0.00005994446,0.00007618416,0.0002029413,0.0001404027,0.00001850437,0.000002780742,0.001008033,0.003378796,0.01661724,0.0002729633,0.00007355894,0.9781486],"study_design_scores_gemma":[0.0004785861,0.0002026028,0.0001671423,0.000278292,0.00003107487,0.00001697379,0.00008640621,0.6386877,0.3563243,0.003055464,0.0002903597,0.0003810189],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003986388,0.0001023015,0.9937209,0.0005987685,0.0002231497,0.0002563885,0.00001186275,0.0004713423,0.0006289163],"genre_scores_gemma":[0.9309876,0.00001184753,0.06846575,0.0001088444,0.00004734599,0.0000534244,9.162031e-7,0.00002080768,0.0003034382],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9777676,"threshold_uncertainty_score":0.9993337,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03388292182735758,"score_gpt":0.311307947499687,"score_spread":0.2774250256723294,"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."}}