{"id":"W4386058569","doi":"10.1587/transfun.2023eal2068","title":"DNN Aided Joint Source-Channel Decoding Scheme for Polar Codes","year":2023,"lang":"en","type":"article","venue":"IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences","topic":"Error Correcting Code Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"Decoding methods; Computer science; Joint (building); Channel (broadcasting); Belief propagation; Algorithm; Factor graph; Polar code; Source code; Polar; Telecommunications; Engineering; 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":[0.0005167464,0.0008289397,0.0005132126,0.0005118864,0.0005404367,0.0005446803,0.0006715668,0.0007944892,0.001883671],"category_scores_gemma":[0.001618568,0.000208778,0.0003257593,0.0005427144,0.0006944355,0.001143295,0.001273661,0.0011199,0.000725202],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006794565,"about_ca_system_score_gemma":0.001857501,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004020683,"about_ca_topic_score_gemma":0.006680553,"domain_scores_codex":[0.9994168,0.0001343693,0.0000323517,0.00009170306,0.0002472123,0.000077483],"domain_scores_gemma":[0.9992763,0.000185233,0.00006399498,0.0001386437,0.0002974838,0.00003842997],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0008534932,0.00009821042,0.001501275,0.0002843862,0.0001013061,0.0004768186,0.0003156346,0.3711499,0.07633604,0.09674402,0.006550293,0.4455887],"study_design_scores_gemma":[0.00002534781,0.00009066178,0.0001278762,0.00001948879,0.00002294623,0.0001694836,0.00002579288,0.9546552,0.02824524,0.01244454,0.004142674,0.00003072914],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01674254,0.0002946074,0.978168,0.0002214647,0.00008684478,0.00004652661,0.0001154819,0.0006156393,0.003709033],"genre_scores_gemma":[0.5255388,0.0004888148,0.4641459,0.0003387173,0.00006984422,0.0001052706,0.0004532116,0.00008274628,0.008776793],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004020683,"threshold_uncertainty_score":0.007994592,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06829875709549948,"score_gpt":0.3181294128443707,"score_spread":0.2498306557488713,"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."}}