{"id":"W4309001612","doi":"10.3390/electronics11223721","title":"Performance Improvement of Polar Codes via UEP Product Coding","year":2022,"lang":"en","type":"article","venue":"Electronics","topic":"Error Correcting Code Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"National Natural Science Foundation of China","keywords":"Polar code; Polar; Algorithm; Decoding methods; Computer science; Coding (social sciences); Additive white Gaussian noise; Concatenated error correction code; Turbo code; Channel (broadcasting); Mathematical optimization; Mathematics; Block code; Telecommunications; Statistics; Physics","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":[0.001004067,0.0009894944,0.000498311,0.0006373813,0.0003079823,0.0008050285,0.0005801874,0.0005485289,0.0009596426],"category_scores_gemma":[0.003863397,0.0002439975,0.0002751702,0.0008781806,0.0009448419,0.0008821868,0.001145741,0.0007332455,0.0003319061],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004666614,"about_ca_system_score_gemma":0.0009586022,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009778053,"about_ca_topic_score_gemma":0.0009833819,"domain_scores_codex":[0.9989702,0.0003906403,0.00003456499,0.0001144682,0.000364484,0.0001256863],"domain_scores_gemma":[0.9985211,0.0006414115,0.0001661882,0.000176619,0.0004525825,0.00004215072],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006706739,0.00008883634,0.001826789,0.0002023103,0.00006980688,0.0004178248,0.0003455598,0.5442579,0.0866136,0.1726021,0.001895139,0.1910094],"study_design_scores_gemma":[0.00001963326,0.000181482,0.0002400223,0.00003300066,0.00002323474,0.0002689412,0.00003075666,0.9452131,0.0385608,0.01261317,0.002784961,0.00003083898],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1118684,0.001085291,0.8707521,0.0003139027,0.00009521587,0.00004506343,0.00006593057,0.0004542473,0.01531986],"genre_scores_gemma":[0.8661903,0.001024239,0.1297876,0.0001225706,0.00004302785,0.00004759021,0.00008698506,0.00004572596,0.002651999],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001004067,"threshold_uncertainty_score":0.005310118,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00903922765785835,"score_gpt":0.2331500131684164,"score_spread":0.224110785510558,"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."}}