{"id":"W2796841874","doi":"10.1109/tit.2018.2873140","title":"Stopping Redundancy Hierarchy Beyond the Minimum Distance","year":2018,"lang":"en","type":"preprint","venue":"IEEE Transactions on Information Theory","topic":"Error Correcting Code Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Division of Mathematical Sciences; University of Illinois at Urbana-Champaign; ITMO University; University of Cambridge; Tallinna Tehnikaülikool; Tartu Ülikool; Nanyang Technological University; Belarusian State University; Saint Petersburg State University; Lunds Universitet; McGill University; Eesti Teadusagentuur; Technion-Israel Institute of Technology; University College Dublin","keywords":"Redundancy (engineering); Row; Mathematics; Code (set theory); Decoding methods; Combinatorics; Upper and lower bounds; Binary erasure channel; Discrete mathematics; Algorithm; Computer science; Coding (social sciences); Statistics; Set (abstract data type)","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001543748,0.0003853794,0.000272399,0.0004633574,0.0007529993,0.0005964729,0.002304413,0.0003035201,0.00005742373],"category_scores_gemma":[0.00004332716,0.000316868,0.0002571645,0.0004703058,0.0002710909,0.001700138,0.00004768993,0.00137218,0.0002729719],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002757496,"about_ca_system_score_gemma":0.0002697316,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001632829,"about_ca_topic_score_gemma":0.00001150381,"domain_scores_codex":[0.9975938,0.0003387585,0.0007314935,0.0003899454,0.0005712375,0.0003747173],"domain_scores_gemma":[0.996591,0.0004862957,0.0005366697,0.001972401,0.0003233331,0.00009034251],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002226659,0.0002146549,0.000003847627,0.0004595937,0.0002653883,0.000004653119,0.04856557,0.0164745,0.0001177949,0.1749402,0.01234212,0.746389],"study_design_scores_gemma":[0.0007679127,0.0004816278,0.00008251293,0.001246593,0.000139831,0.0001010379,0.001182623,0.1740077,0.04453759,0.7310587,0.04419596,0.00219795],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0007910672,0.00005814519,0.9771075,0.001060668,0.005182714,0.0006974241,0.00005131935,0.001402255,0.01364897],"genre_scores_gemma":[0.9617196,0.00009413697,0.03406807,0.002341083,0.0001749701,0.0005049052,0.000016559,0.00003557326,0.001045099],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9609286,"threshold_uncertainty_score":0.9999284,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01348879551729385,"score_gpt":0.2542901375572471,"score_spread":0.2408013420399533,"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."}}