{"id":"W2056207853","doi":"10.1109/tcomm.2015.2424416","title":"Update-Efficient Error-Correcting Product-Matrix Codes","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Communications","topic":"Advanced Data Storage Technologies","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"National Science Council; Syracuse University","keywords":"Vandermonde matrix; Computer science; Decoding methods; Algorithm; Overhead (engineering); Fountain code; Error detection and correction; Encoding (memory); Encoder; Luby transform code; Hamming code; Node (physics); Concatenated error correction code; Block code; Reed–Solomon error correction; Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006368446,0.0005483854,0.000423948,0.0006023791,0.0003787498,0.0005227537,0.00084417,0.0004271837,0.001178405],"category_scores_gemma":[0.003537389,0.0001695677,0.0002158909,0.0009059341,0.0005672608,0.001111564,0.0005600333,0.0006032556,0.0005717937],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005105605,"about_ca_system_score_gemma":0.0008724265,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001371576,"about_ca_topic_score_gemma":0.002165902,"domain_scores_codex":[0.9993092,0.0001597903,0.00004766716,0.00009311644,0.0003214486,0.00006885707],"domain_scores_gemma":[0.9974695,0.0007985514,0.000419553,0.0005463359,0.000716329,0.00004975496],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005888736,0.0001253848,0.001264255,0.0003320394,0.00006500213,0.0004226388,0.0004227082,0.3329549,0.1112612,0.1693019,0.004178837,0.3790822],"study_design_scores_gemma":[0.00004591475,0.000233788,0.000277606,0.00003639062,0.00002568897,0.0005283621,0.00003903801,0.8943462,0.07719496,0.02038325,0.006844064,0.0000447009],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0498568,0.0005164391,0.9458116,0.0001597305,0.00005013365,0.00007478503,0.0001062093,0.0007939974,0.002630319],"genre_scores_gemma":[0.5693576,0.0005996513,0.4216837,0.0001204096,0.00005904097,0.0001294897,0.0002664251,0.00007453264,0.00770919],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001371576,"threshold_uncertainty_score":0.003942192,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.067804828299535,"score_gpt":0.3306793863381466,"score_spread":0.2628745580386116,"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."}}