{"id":"W2760103885","doi":"10.1145/3127479.3131623","title":"Latency reduction and load balancing in coded storage systems","year":2017,"lang":"en","type":"article","venue":"","topic":"Advanced Data Storage Technologies","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Latency (audio); Erasure code; Distributed data store; Load balancing (electrical power); Computer network; Computer data storage; Distributed computing; Erasure; Real-time computing; Operating system; Decoding methods; Telecommunications","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":[0.0002180969,0.00007757771,0.0001158345,0.00006612446,0.0001655169,0.0002534123,0.0007405337,0.00005640813,0.000001108702],"category_scores_gemma":[0.0001582313,0.00006820558,0.000007821443,0.00006795879,0.00007449308,0.001416413,0.0005200111,0.000106077,0.00001210203],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009390194,"about_ca_system_score_gemma":0.00002492989,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003669623,"about_ca_topic_score_gemma":0.00004300625,"domain_scores_codex":[0.9992789,0.00001355551,0.0001241423,0.0002953195,0.0001253938,0.0001627036],"domain_scores_gemma":[0.9988211,0.00001483506,0.00009998991,0.001007034,0.00003211632,0.00002494363],"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":[0.00002522077,0.000136353,0.01851941,0.0002030368,0.00002940224,0.0005337822,0.002527211,0.003100367,0.05750997,0.692536,0.004233524,0.2206457],"study_design_scores_gemma":[0.003281504,0.0003400535,0.1050688,0.0005336498,0.00001200436,0.0007485111,0.002371581,0.7951722,0.01930962,0.06277707,0.008569222,0.001815754],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2347754,0.0004838723,0.7589421,0.0007304048,0.0007816337,0.0002049556,0.000001886979,0.0006363926,0.003443293],"genre_scores_gemma":[0.9675635,0.00005065834,0.03197275,0.000007283083,0.00001699902,0.000009226944,4.408187e-7,0.000003519959,0.0003756471],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7920718,"threshold_uncertainty_score":0.2781343,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01964613170326899,"score_gpt":0.2612589017370804,"score_spread":0.2416127700338115,"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."}}