{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001615403,0.0001440716,0.0001863565,0.0001373046,0.0005715596,0.0001552158,0.0007332631,0.00005294642,0.00002303822],"category_scores_gemma":[0.00004146101,0.0001095247,0.00006084561,0.001297521,0.00003431555,0.0003532213,0.0001198407,0.0001493547,0.000004337347],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005346349,"about_ca_system_score_gemma":0.00005355515,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003045067,"about_ca_topic_score_gemma":0.00004739604,"domain_scores_codex":[0.99875,0.00006242768,0.0002631721,0.0003081263,0.0002018535,0.0004144561],"domain_scores_gemma":[0.9983902,0.000773161,0.00007797143,0.0004507517,0.0001759115,0.0001320115],"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":[0.00007601752,0.0000630767,0.002035356,0.00002324654,0.00002783566,0.000004218229,0.0007381034,0.0008976076,0.001856734,0.3270178,0.001518642,0.6657414],"study_design_scores_gemma":[0.004315128,0.0007334229,0.01348604,0.0004480181,0.00004500814,0.00005241403,0.0001690834,0.6950327,0.02085787,0.003619978,0.2598682,0.001372215],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001577739,0.0007058201,0.984735,0.0006316332,0.0001278904,0.0003573541,2.605674e-7,0.0002415158,0.01162286],"genre_scores_gemma":[0.70583,0.0003902907,0.292988,0.0003336216,0.0001002873,0.00001037472,0.00000211394,0.000008089274,0.000337184],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7042523,"threshold_uncertainty_score":0.4466289,"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."}}