{"id":"W2041269601","doi":"10.1109/vetecf.2010.5594492","title":"Reliable Network Coded MAC in Vehicular Ad-Hoc Networks","year":2010,"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 Toronto","funders":"","keywords":"Linear network coding; Computer science; Computer network; Wireless ad hoc network; Network packet; Node (physics); Coding (social sciences); Collision; Reliability (semiconductor); Vehicular ad hoc network; Repetition code; Random access; Decoding methods; Wireless; Computer security; Algorithm; Telecommunications; Block code; Engineering","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":[0.002089186,0.0006584677,0.0005672548,0.0009123116,0.0003361907,0.0007813906,0.0008999123,0.0007374391,0.0004363454],"category_scores_gemma":[0.006709629,0.0003879923,0.0001945254,0.001003916,0.00107958,0.001083302,0.0007587817,0.0005976922,0.0001400277],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008131864,"about_ca_system_score_gemma":0.001018725,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003676769,"about_ca_topic_score_gemma":0.002536237,"domain_scores_codex":[0.9985643,0.0006778499,0.00004563822,0.00009151602,0.0005114757,0.0001092513],"domain_scores_gemma":[0.9959371,0.002879189,0.0003830028,0.0002591056,0.0004913224,0.00005034241],"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":[0.0001151002,0.00001969075,0.0002802347,0.0001172476,0.00001836586,0.0001456645,0.0001256704,0.939172,0.002613396,0.03142434,0.0006064862,0.02536179],"study_design_scores_gemma":[0.00001493751,0.00004250494,0.00007448867,0.00001028785,0.000007282601,0.00003095965,0.000014754,0.9872798,0.0006619444,0.01128444,0.0005713236,0.000007291885],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04982014,0.001596813,0.9449424,0.000259085,0.00006534446,0.00006739506,0.00004691622,0.0002146884,0.002987254],"genre_scores_gemma":[0.9009091,0.00124033,0.09501268,0.00008676525,0.00009864908,0.0002204159,0.00007110314,0.00003082863,0.00233005],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003676769,"threshold_uncertainty_score":0.01104879,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01762592602073842,"score_gpt":0.2541584277837513,"score_spread":0.2365325017630128,"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."}}