{"id":"W2104730183","doi":"10.1109/vetecs.2009.5073564","title":"Fountain Codes with XOR of Encoded Packets for Broadcasting and Source Independent Backbone in Multi-Hop Networks Using Network Coding","year":2009,"lang":"en","type":"article","venue":"","topic":"Cooperative Communication and Network Coding","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Linear network coding; Computer network; Fountain code; Network packet; Flooding (psychology); Broadcasting (networking); Asynchronous communication; Coding (social sciences); Source code; Wireless ad hoc network; Broadcast communication network; Wireless network; Wireless; Decoding methods; Algorithm; Telecommunications; Block code; Concatenated error correction code; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0006904122,0.000321546,0.0003314998,0.0005467682,0.000431445,0.0004911876,0.0006532329,0.0006353974,0.0005904399],"category_scores_gemma":[0.002854508,0.0001608258,0.0002830172,0.0006546602,0.0008975617,0.001131372,0.0008256205,0.0005918184,0.0001333844],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006843901,"about_ca_system_score_gemma":0.0008206313,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002074264,"about_ca_topic_score_gemma":0.001852558,"domain_scores_codex":[0.9995841,0.0001622878,0.00001663887,0.00003715497,0.0001576321,0.0000421098],"domain_scores_gemma":[0.9990164,0.0005362227,0.0001284893,0.0001368139,0.0001447868,0.00003727082],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002839415,0.00007467993,0.0008241539,0.0001157771,0.0000296118,0.000252016,0.0003296336,0.5680473,0.01736592,0.3121743,0.001276163,0.09922646],"study_design_scores_gemma":[0.00002578812,0.00006964792,0.00008459286,0.00001306492,0.0000099688,0.0000807337,0.00001904028,0.957451,0.005207869,0.03564869,0.001375865,0.00001373812],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05740369,0.0003002685,0.939035,0.0001740598,0.00002389722,0.00005175655,0.00002674624,0.0001604964,0.002824157],"genre_scores_gemma":[0.7289288,0.0004193907,0.2670962,0.0001026213,0.00003402046,0.0002025022,0.00007941369,0.0000583917,0.003078685],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002074264,"threshold_uncertainty_score":0.004965663,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0646761389017575,"score_gpt":0.3045168659467208,"score_spread":0.2398407270449633,"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."}}