{"id":"W2942699488","doi":"10.1109/access.2019.2913339","title":"Construction of Multi-State Capacity-Approaching Variable-Length Constrained Sequence Codes With State-Independent Decoding","year":2019,"lang":"en","type":"article","venue":"IEEE Access","topic":"DNA and Biological Computing","field":"Biochemistry, Genetics and Molecular Biology","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates; Alberta Innovates - Technology Futures","keywords":"Decoding methods; Encoder; Huffman coding; Computer science; Constraint (computer-aided design); Sequence (biology); Algorithm; State (computer science); Coding (social sciences); Variable (mathematics); Theoretical computer science; Mathematics; Data compression","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":[0.0004044306,0.0001675966,0.0002172278,0.00004556456,0.00007061421,0.00005649813,0.0002951923,0.0001072611,0.00001153671],"category_scores_gemma":[0.00003464966,0.0001263452,0.00004380407,0.0001212222,0.0001783875,0.00001782542,0.00009809256,0.000157956,0.000002220575],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001705393,"about_ca_system_score_gemma":0.00006987444,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001314988,"about_ca_topic_score_gemma":0.00005037939,"domain_scores_codex":[0.9988412,0.00007613268,0.0002727714,0.0003855052,0.0001414003,0.0002830066],"domain_scores_gemma":[0.9993237,0.0000472415,0.0002206165,0.000212633,0.0001284225,0.00006739238],"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":[0.0001484835,0.00005340715,0.07248471,0.00007452522,0.00008349818,0.000004470558,0.00005062598,0.003818238,0.9153259,0.0001567613,0.000002995728,0.007796422],"study_design_scores_gemma":[0.001442228,0.0004153333,0.00307839,0.0001011941,0.00002344238,0.00009177928,0.0001812574,0.007143958,0.9866328,0.0002973168,0.0001807701,0.0004114895],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8832476,0.00002413299,0.1153505,0.000005842929,0.000187815,0.000219054,0.00003554065,0.00002018527,0.0009093676],"genre_scores_gemma":[0.9785858,0.00001780074,0.02119691,0.00007494262,0.00003522772,0.000004072723,0.00003038313,0.00001201341,0.00004280455],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09533824,"threshold_uncertainty_score":0.515221,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04514387786130067,"score_gpt":0.2879050768249769,"score_spread":0.2427611989636762,"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."}}