{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007315928,0.0001153064,0.0001501103,0.0001071981,0.0002558979,0.00002238783,0.0009985953,0.00001621966,0.00001429899],"category_scores_gemma":[0.00002385387,0.0001254505,0.00004874437,0.000490544,0.00002280577,0.0001978567,0.0005595037,0.0003585349,0.000002570597],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003155355,"about_ca_system_score_gemma":0.0001931047,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001983443,"about_ca_topic_score_gemma":0.00001216943,"domain_scores_codex":[0.998626,0.00004967516,0.000227345,0.0003297073,0.0003770285,0.0003902327],"domain_scores_gemma":[0.9991026,0.0000322621,0.000166901,0.0006012908,0.00006763472,0.0000293088],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00002664349,0.0002478202,0.004613255,0.00009096268,0.00004756354,0.00000470854,0.001521358,0.000650582,0.6499805,0.01572898,0.001527093,0.3255605],"study_design_scores_gemma":[0.0001828686,0.001799295,0.000413084,0.00001203952,0.000008705284,0.00005149279,0.00002899298,0.09312185,0.8828828,0.001922829,0.01926709,0.0003089797],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8460163,0.002969409,0.147098,0.0007046922,0.0005551629,0.0005476403,0.000003619421,0.000822873,0.00128229],"genre_scores_gemma":[0.9890937,0.0001117489,0.01040931,0.0001101109,0.00002726161,0.00005475203,0.000002533557,0.00001341288,0.0001771458],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3252515,"threshold_uncertainty_score":0.5115722,"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."}}