{"id":"W2615444200","doi":"10.1109/sips.2017.8109987","title":"Efficient bit-channel reliability computation for multi-mode polar code encoders and decoders","year":2017,"lang":"en","type":"preprint","venue":"","topic":"Error Correcting Code Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Encoder; Polar code; Reliability (semiconductor); Algorithm; Computation; Code (set theory); Channel (broadcasting); Low-density parity-check code; Decoding methods; Code rate; Parity bit; Computer engineering; Error detection and correction; Set (abstract data type); Telecommunications","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001493937,0.0005185897,0.00058663,0.0002824232,0.0005463432,0.0006025176,0.001841153,0.0004907938,0.000001008036],"category_scores_gemma":[0.0008000391,0.0005132278,0.0002421596,0.00008773643,0.0002079328,0.0001668196,0.002421619,0.0006307307,0.000004917452],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002576608,"about_ca_system_score_gemma":0.0002963097,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001612887,"about_ca_topic_score_gemma":0.0006813234,"domain_scores_codex":[0.9965968,0.0001497836,0.0005815719,0.001734247,0.0003993112,0.0005382532],"domain_scores_gemma":[0.9963917,0.00042687,0.000617128,0.00186352,0.0005000596,0.0002007495],"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":[0.0001595979,0.001347921,0.003710202,0.002628861,0.0002693382,0.00002517688,0.01683156,0.8932964,0.00100492,0.008208878,0.009877741,0.06263939],"study_design_scores_gemma":[0.0004227806,0.00008523292,0.0006770249,0.0001747026,0.00002609102,0.000008313262,0.00005453357,0.9797432,0.001196246,0.01694024,0.00009745919,0.0005742071],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02760248,0.0001507806,0.9656278,0.001110105,0.001606247,0.001860571,0.00006416671,0.001603729,0.000374144],"genre_scores_gemma":[0.4764899,0.00001759582,0.5230482,0.0001248968,0.00003477277,0.0001445236,0.0000198437,0.00003035751,0.00008996749],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4488874,"threshold_uncertainty_score":0.999732,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06047249763726605,"score_gpt":0.3589647498871124,"score_spread":0.2984922522498464,"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."}}