{"id":"W2063293063","doi":"10.1109/infcom.2010.5462025","title":"Diversity-Rate Trade-off in Erasure Networks","year":2010,"lang":"en","type":"article","venue":"","topic":"Cooperative Communication and Network Coding","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Erasure; Erasure code; Computer science; Linear network coding; Multicast; Computer network; Online codes; Node (physics); Distributed computing; Algorithm; Decoding methods; Block code; Network packet; Linear code","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":[0.0004278543,0.00007490624,0.0000845868,0.00006072326,0.0003910214,0.00007054683,0.001039167,0.00005433722,0.00009573824],"category_scores_gemma":[0.00002091933,0.00006769317,0.00002835602,0.0004320306,0.00002794901,0.0003036298,0.00147299,0.000389036,0.00001849981],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001155933,"about_ca_system_score_gemma":0.00001778234,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007808091,"about_ca_topic_score_gemma":0.001199769,"domain_scores_codex":[0.9993591,0.00009769993,0.0001106689,0.000183221,0.00007887185,0.0001704437],"domain_scores_gemma":[0.9992679,0.0001002232,0.00002443782,0.0005224142,0.00002440043,0.00006059482],"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":[0.000005941623,0.0001336872,0.04150893,0.000002217595,0.0000123013,0.00001726374,0.003885004,0.001319056,0.001682795,0.5548932,0.006731359,0.3898082],"study_design_scores_gemma":[0.0004460469,0.00002341891,0.1633628,0.00001018517,0.000001585118,0.000005009997,0.00002325035,0.8041152,0.0001849623,0.0008062991,0.03077365,0.0002475624],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2935931,0.0004997783,0.6423317,0.0158881,0.001508204,0.0003152357,3.385984e-7,0.0004864655,0.04537705],"genre_scores_gemma":[0.9944714,0.0002737167,0.003597533,0.001218611,0.00004312021,0.00000172486,6.997922e-7,0.000002792066,0.0003904375],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8027961,"threshold_uncertainty_score":0.300746,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02670040501420033,"score_gpt":0.2483570866233029,"score_spread":0.2216566816091025,"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."}}