{"id":"W2775074672","doi":"10.1109/iemcon.2017.8117161","title":"Performance of NMS decoding of SC polar codes based density evolution","year":2017,"lang":"en","type":"article","venue":"","topic":"Error Correcting Code Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Decoding methods; Algorithm; Function (biology); Reduction (mathematics); Sequential decoding; Product (mathematics); Computer science; Berlekamp–Welch algorithm; Polar; Nonlinear system; Probability density function; Process (computing); Mathematics; Physics; Statistics; Block code","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":[0.000666172,0.0005962819,0.0004672392,0.000428768,0.0004548318,0.0007105942,0.0004395093,0.0007563402,0.001073147],"category_scores_gemma":[0.003086944,0.0001176667,0.0002009202,0.0005128659,0.000621256,0.0007788241,0.0006899931,0.0005016053,0.0002991431],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005822079,"about_ca_system_score_gemma":0.0008644283,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002131777,"about_ca_topic_score_gemma":0.001411501,"domain_scores_codex":[0.9991641,0.0002304001,0.00003492855,0.00009360495,0.0003807567,0.00009622639],"domain_scores_gemma":[0.9987919,0.0005564215,0.0000943458,0.0001292021,0.0003904059,0.00003787422],"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":[0.002771689,0.0001830867,0.007848362,0.000546199,0.0001953609,0.0004655213,0.0005326182,0.5326747,0.2084538,0.06243719,0.001831636,0.1820598],"study_design_scores_gemma":[0.00002256355,0.0001727835,0.0008891126,0.00002359685,0.00001912359,0.0002052853,0.00003385362,0.919265,0.07533891,0.003233935,0.000768472,0.00002721195],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.7012043,0.001459972,0.2673722,0.0005771522,0.000150316,0.0000543373,0.0001843674,0.001031124,0.02796626],"genre_scores_gemma":[0.9746253,0.0002553897,0.0230581,0.00004351708,0.00001559216,0.00001846055,0.0001327921,0.00003816665,0.001812702],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.002131777,"threshold_uncertainty_score":0.004238725,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02235162601650961,"score_gpt":0.2708443826956378,"score_spread":0.2484927566791282,"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."}}