{"id":"W2151614040","doi":"10.1109/cwit.2007.375706","title":"Rank-Metric Codes for Priority Encoding Transmission with Network Coding","year":2007,"lang":"en","type":"article","venue":"","topic":"Cooperative Communication and Network Coding","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Linear network coding; Network packet; Coding (social sciences); Encoding (memory); Computer network; Theoretical computer science; Broadcasting (networking); Packet loss; Distributed computing; Forward error correction; Algorithm; Decoding methods; Mathematics; Artificial intelligence","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":[0.001702608,0.0007195844,0.0006387365,0.0008353521,0.0007863996,0.00119361,0.0008675676,0.000787991,0.002509749],"category_scores_gemma":[0.006452741,0.0002461293,0.0002137206,0.001186129,0.0009987031,0.001261225,0.001506171,0.001301117,0.0009878846],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009453398,"about_ca_system_score_gemma":0.001408371,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001392447,"about_ca_topic_score_gemma":0.001662828,"domain_scores_codex":[0.9982894,0.0006392808,0.00009528249,0.000143161,0.0006270895,0.0002057366],"domain_scores_gemma":[0.996348,0.001250623,0.0005650884,0.000693879,0.0009991953,0.0001431657],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0009108712,0.0001429102,0.000458169,0.0003487358,0.00003783468,0.0004467522,0.00043779,0.10435,0.04553467,0.5555281,0.008896046,0.2829081],"study_design_scores_gemma":[0.0001354761,0.0004365512,0.0001866233,0.0000859549,0.00003445685,0.0006506638,0.00008373433,0.7859096,0.03281441,0.1601609,0.01938718,0.0001144479],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01197494,0.0005357524,0.9807845,0.0002996167,0.00009389695,0.0001164116,0.00008327985,0.0004607564,0.005650832],"genre_scores_gemma":[0.616807,0.0007181984,0.3733737,0.0004179023,0.000206205,0.0004017267,0.0002676508,0.0001330443,0.007674515],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002509749,"threshold_uncertainty_score":0.009004354,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0359961827801999,"score_gpt":0.2968004747500106,"score_spread":0.2608042919698108,"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."}}