{"id":"W3197054949","doi":"10.1109/tvt.2021.3109868","title":"Repair Delay Analysis of Mobile Storage Systems Using Erasure Codes and Relay Cooperation","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Caching and Content Delivery","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"National Natural Science Foundation of China","keywords":"Erasure; Relay; Erasure code; Computer science; Data loss; Reliability (semiconductor); Computer network; Base station; Path loss; Mobile device; Decoding methods; Real-time computing; Reliability engineering; Engineering; Telecommunications; Wireless","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":[0.0001998107,0.0001183632,0.0003238865,0.00064604,0.0001826365,0.00003981413,0.0001617562,0.0002095235,0.000004023405],"category_scores_gemma":[0.000007678145,0.000119368,0.0001549156,0.001822837,0.00007413266,0.0001442283,0.000005334981,0.0002436386,0.000001689346],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005626686,"about_ca_system_score_gemma":0.00006246051,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001149971,"about_ca_topic_score_gemma":0.0000983039,"domain_scores_codex":[0.9989195,0.0001144381,0.0002703723,0.0003904693,0.0001603921,0.0001447611],"domain_scores_gemma":[0.9990176,0.00005177434,0.00008016827,0.0005995996,0.0002168868,0.00003397292],"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":[0.000005310307,0.00011715,0.0001399709,0.0000184056,0.0007056046,0.00008464979,0.0001008798,0.9106115,0.08390391,0.001741878,0.000007979182,0.002562814],"study_design_scores_gemma":[0.0001792831,0.00009996636,0.00003171669,0.00003518808,0.0004291551,0.000125746,0.0001403928,0.9731504,0.02547299,0.000008795675,0.0001982096,0.0001281098],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4649366,0.000710184,0.533881,0.00006246559,0.0001441556,0.00006454974,0.000008979703,0.0001873736,0.000004745399],"genre_scores_gemma":[0.9967413,0.0001050535,0.00298979,0.00003616521,0.000004992524,0.00002604344,0.000002924048,0.000007256625,0.00008649812],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5318047,"threshold_uncertainty_score":0.4867687,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01257336653552188,"score_gpt":0.2327537328803898,"score_spread":0.2201803663448679,"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."}}