{"id":"W2554527937","doi":"10.1109/tcsi.2016.2619324","title":"A Fast Polar Code List Decoder Architecture Based on Sphere Decoding","year":2016,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits and Systems I Regular Papers","topic":"Error Correcting Code Techniques","field":"Computer Science","cited_by":100,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Decoding methods; Polar code; Computer science; List decoding; Throughput; Polar; Algorithm; Error detection and correction; Code (set theory); Sequential decoding; Soft-decision decoder; Concatenated error correction code; Telecommunications; Block code; Set (abstract data type); 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002437975,0.0007160532,0.000511487,0.0008827586,0.0005844824,0.001033976,0.0008661147,0.0006790057,0.004274144],"category_scores_gemma":[0.0006648159,0.0002410362,0.000256291,0.0007602764,0.0003421911,0.001064041,0.0007028165,0.0006668771,0.004192599],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005979565,"about_ca_system_score_gemma":0.001427463,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002745976,"about_ca_topic_score_gemma":0.004168554,"domain_scores_codex":[0.999694,0.00005450751,0.00001986089,0.00004554803,0.0001484597,0.00003764529],"domain_scores_gemma":[0.9995511,0.0000891845,0.00003198403,0.00006375054,0.000233932,0.00003008176],"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":[0.001028956,0.0001730785,0.00146145,0.0004695959,0.0001137328,0.0007479828,0.0002547659,0.06748014,0.3552479,0.05707245,0.02242943,0.4935205],"study_design_scores_gemma":[0.0001320408,0.0006408475,0.0005335672,0.00005985185,0.0000784321,0.001122695,0.00005535687,0.6041421,0.3399705,0.008042664,0.04511905,0.0001029664],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.021571,0.000684902,0.9576756,0.0002828138,0.0002318049,0.0001681122,0.0003676467,0.006659545,0.01235864],"genre_scores_gemma":[0.2939396,0.001016674,0.6853792,0.0004530414,0.0002128747,0.0001740966,0.001143263,0.0002463895,0.01743488],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004274144,"threshold_uncertainty_score":0.01429838,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01595336298905812,"score_gpt":0.2303364820129234,"score_spread":0.2143831190238653,"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."}}