{"id":"W2051270424","doi":"10.1109/lcomm.2011.101811.111480","title":"A Simplified Successive-Cancellation Decoder for Polar Codes","year":2011,"lang":"en","type":"article","venue":"IEEE Communications Letters","topic":"Error Correcting Code Techniques","field":"Computer Science","cited_by":457,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Decoding methods; Computer science; Block code; Algorithm; Latency (audio); Soft-decision decoder; Polar; Bit error rate; Concatenated error correction code; Linear code; Polar code; Telecommunications","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":[0.0004064586,0.0007134189,0.0006582123,0.0005419563,0.0004883419,0.0009037564,0.00101648,0.0005206284,0.004024907],"category_scores_gemma":[0.001558627,0.0002956539,0.0004989547,0.0005651246,0.0005087136,0.0008871285,0.0008868364,0.001495623,0.0034789],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004492599,"about_ca_system_score_gemma":0.001852763,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002070561,"about_ca_topic_score_gemma":0.004076391,"domain_scores_codex":[0.9994055,0.00007543015,0.00003308947,0.00007044904,0.0003657884,0.00004975563],"domain_scores_gemma":[0.9993144,0.000134292,0.00004589571,0.0001816388,0.0002885067,0.00003521811],"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":[0.0007531113,0.0001067855,0.0006270917,0.0005268406,0.00008822923,0.0007581007,0.000188923,0.1185872,0.3030799,0.1617576,0.01295993,0.4005663],"study_design_scores_gemma":[0.0001589245,0.0003078239,0.0004833145,0.00008351642,0.0001071341,0.001938127,0.00005101242,0.6633887,0.2192892,0.04206871,0.07195946,0.0001640988],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004932541,0.0001978071,0.987484,0.000122155,0.0001551323,0.0001073945,0.0002245785,0.0008659079,0.005910379],"genre_scores_gemma":[0.1486066,0.00108315,0.8304265,0.0003916879,0.0002034105,0.0002576625,0.001161543,0.000234246,0.01763529],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004024907,"threshold_uncertainty_score":0.01346463,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09236214429013771,"score_gpt":0.3130310112581669,"score_spread":0.2206688669680292,"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."}}