{"id":"W2097025603","doi":"10.1109/18.930944","title":"Relationships between different error-correcting capabilities of a code","year":2001,"lang":"en","type":"article","venue":"IEEE Transactions on Information Theory","topic":"DNA and Biological Computing","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Saint Mary's University","funders":"","keywords":"Hamming distance; Error detection and correction; Hamming code; Levenshtein distance; Algorithm; Channel (broadcasting); Computer science; Type (biology); Mathematics; Code (set theory); Discrete mathematics; Block code; Decoding methods; 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":[],"consensus_categories":[],"category_scores_codex":[0.0003728941,0.0001030008,0.0001126435,0.00007480472,0.0001751776,0.00001395857,0.00009852601,0.0001311931,0.00004568173],"category_scores_gemma":[0.00005660574,0.00008300748,0.0000952848,0.00008992718,0.00007300015,0.00001881947,0.000002273395,0.0001706482,0.00001681519],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001559965,"about_ca_system_score_gemma":0.00001614547,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004824478,"about_ca_topic_score_gemma":0.000007515008,"domain_scores_codex":[0.9992135,0.0001462495,0.0003266922,0.0000928749,0.00009213138,0.0001286073],"domain_scores_gemma":[0.99946,0.0001439349,0.0001174342,0.0001632171,0.00007110981,0.00004427908],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002507246,0.0007992705,0.04718061,0.0003897185,0.0007609758,0.000001569728,0.01045261,0.07063784,0.08187931,0.007174956,0.0005244492,0.7776914],"study_design_scores_gemma":[0.002038762,0.001747393,0.03073653,0.0001688344,0.0001534218,0.00005523429,0.01122347,0.002145922,0.9344122,0.007696689,0.008654019,0.0009674648],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6004096,0.000008798166,0.3977929,0.00002421426,0.0001137965,0.00007920478,0.00002426148,0.00002035223,0.001526881],"genre_scores_gemma":[0.9994019,0.00001256251,0.0001760871,0.00006426583,0.00004615912,0.00001295467,0.00004114561,0.000004817231,0.0002401557],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8525329,"threshold_uncertainty_score":0.3384947,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02858556006161752,"score_gpt":0.2536248534371637,"score_spread":0.2250392933755462,"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."}}