{"id":"W2802514696","doi":"10.1109/tcomm.2018.2832207","title":"Decoder Partitioning: Towards Practical List Decoding of Polar Codes","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Communications","topic":"Error Correcting Code Techniques","field":"Computer Science","cited_by":51,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung","keywords":"Decoding methods; Computer science; List decoding; Sequential decoding; Polar; Algorithm; Concatenated error correction code; Electronic engineering; Theoretical computer science; Parallel computing; Block code; Engineering","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":[0.0005218005,0.0001559297,0.0001972608,0.0002485015,0.0007471206,0.0001070522,0.001644823,0.0001046079,0.00006755211],"category_scores_gemma":[0.00009136981,0.0001679334,0.0001245447,0.0007168051,0.0004151534,0.0005967544,0.00003208081,0.0004351085,0.00006474969],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009602477,"about_ca_system_score_gemma":0.0001996122,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003050373,"about_ca_topic_score_gemma":0.001103743,"domain_scores_codex":[0.9985056,0.000272942,0.0004199541,0.0002892932,0.0002665105,0.0002457105],"domain_scores_gemma":[0.9959292,0.000640692,0.0001826672,0.002727168,0.0004197147,0.0001005111],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001916395,0.009313012,0.001773921,0.0001498438,0.0008265071,0.00001572836,0.02341824,0.0008858643,0.06622728,0.4963323,0.01622677,0.3846388],"study_design_scores_gemma":[0.000695212,0.001068651,0.0008358983,0.0003491894,0.0001362521,0.0001854831,0.0003974564,0.3154645,0.6405299,0.01974884,0.01962496,0.0009636087],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002505339,0.0000439842,0.9832316,0.004667604,0.0003369968,0.0001838878,0.00001619353,0.0006235609,0.008390844],"genre_scores_gemma":[0.6493921,0.0000833818,0.3501804,0.0001644237,0.00001729476,0.00005106707,0.000001707457,0.00001248569,0.00009706224],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6468868,"threshold_uncertainty_score":0.6848126,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08444422761163856,"score_gpt":0.3729335643544408,"score_spread":0.2884893367428022,"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."}}