{"id":"W2123164976","doi":"10.1109/cwit.2009.5069539","title":"Convolutional codes for channels with deletion errors","year":2009,"lang":"en","type":"article","venue":"","topic":"DNA and Biological Computing","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Convolutional code; Viterbi algorithm; Trellis (graph); Sequential decoding; Computer science; Algorithm; Iterative Viterbi decoding; Viterbi decoder; Soft output Viterbi algorithm; Decoding methods; Code word; List decoding; Computational complexity theory; Theoretical computer science; Concatenated error correction code; Block code","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00006604104,0.00006157213,0.00005505756,0.000008246548,0.00005584196,0.000008558964,0.00005242142,0.00006086257,0.000009102602],"category_scores_gemma":[0.0000181974,0.00003936973,0.00003347525,0.00002396934,0.00002615528,8.744718e-7,0.000007651777,0.00001920795,0.000002034222],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000003168962,"about_ca_system_score_gemma":0.00001288806,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001605102,"about_ca_topic_score_gemma":0.000002558289,"domain_scores_codex":[0.999612,0.000008976855,0.00006279121,0.000153754,0.00003657798,0.000125934],"domain_scores_gemma":[0.9998312,0.00000611557,0.00002146869,0.00005655473,0.00005531135,0.00002933738],"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":[0.0003893057,0.000109105,0.001892834,0.000006470553,0.00003227523,9.711555e-7,0.000007571839,0.0005746354,0.975063,0.008243807,0.006575503,0.007104487],"study_design_scores_gemma":[0.002550596,0.009504107,0.02568865,0.00002801187,0.00003272087,0.00005677592,0.0001053611,0.004535956,0.7705725,0.004600659,0.1815498,0.0007747575],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.912536,0.00008604033,0.0838848,0.0007592161,0.00004587506,0.0001452948,0.00000527393,0.00001996714,0.002517508],"genre_scores_gemma":[0.9949453,0.000004716475,0.002947134,0.0009302276,0.000189051,0.000003146399,0.000130746,0.000002507395,0.0008472048],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2044905,"threshold_uncertainty_score":0.1605451,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01486159433858252,"score_gpt":0.2547493658184428,"score_spread":0.2398877714798602,"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."}}