{"id":"W2128555802","doi":"10.1109/tit.2009.2032817","title":"On Metrics for Error Correction in Network Coding","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Information Theory","topic":"Cooperative Communication and Network Coding","field":"Computer Science","cited_by":169,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Linear network coding; Error detection and correction; Subspace topology; Computer science; Algorithm; Constant-weight code; Metric (unit); Coding (social sciences); Variable-length code; Theoretical computer science; Decoding methods; Topology (electrical circuits); Linear code; Mathematics; Block code; Computer network; Artificial intelligence; Statistics","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":[0.0068773,0.002948845,0.001585996,0.0046102,0.001493093,0.00367198,0.002297366,0.003059837,0.003107969],"category_scores_gemma":[0.03170494,0.0005415786,0.001107169,0.004529339,0.006356724,0.009380047,0.005299162,0.00505221,0.001062365],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004517328,"about_ca_system_score_gemma":0.001616384,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002303312,"about_ca_topic_score_gemma":0.0008786698,"domain_scores_codex":[0.9925672,0.003617013,0.0004226741,0.0008593306,0.002080952,0.0004528929],"domain_scores_gemma":[0.9773453,0.01509345,0.001784271,0.00214715,0.002965859,0.0006639868],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000027521,0.000009567743,0.0001594905,0.00008111118,0.00001587757,0.00003743605,0.0001034688,0.03961077,0.0005285803,0.9431928,0.001313375,0.01491995],"study_design_scores_gemma":[0.000006602794,0.00006875081,0.0001364921,0.00008401911,0.00001115296,0.00007852248,0.00004071968,0.1355433,0.0007846802,0.856435,0.006774958,0.0000358593],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009786433,0.006016463,0.9629537,0.001392048,0.0003268282,0.00008513058,0.0002416229,0.0001892018,0.01900842],"genre_scores_gemma":[0.5658817,0.01846983,0.3891892,0.001380926,0.002181491,0.0008876274,0.001257942,0.0007776365,0.01997358],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0068773,"threshold_uncertainty_score":0.03637111,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02773562526010384,"score_gpt":0.2775726853735803,"score_spread":0.2498370601134765,"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."}}