{"id":"W2005480575","doi":"10.14778/1920841.1920847","title":"Database replication","year":2010,"lang":"en","type":"article","venue":"Proceedings of the VLDB Endowment","topic":"Distributed systems and fault tolerance","field":"Computer Science","cited_by":47,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Branco Weiss Fellowship – Society in Science; Eidgenössische Technische Hochschule Zürich; McGill University","keywords":"Computer science; Scalability; Replication (statistics); Distributed computing; Eventual consistency; Database transaction; Consistency (knowledge bases); Fault tolerance; Overhead (engineering); Distributed database; Cloud computing; Database; Transaction processing; Data consistency; Weak consistency; Concurrency control; Strong consistency; Consistency model; Operating system; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003684141,0.00007403183,0.00008892407,0.00002464716,0.00007878659,0.00007130908,0.001214789,0.00002803308,0.000005339858],"category_scores_gemma":[0.00008963555,0.00004883683,0.0000490164,0.0002491422,0.00003430751,0.0003265763,0.0003204315,0.0001261411,0.00001229899],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001326218,"about_ca_system_score_gemma":0.00001715233,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003387851,"about_ca_topic_score_gemma":0.000002453319,"domain_scores_codex":[0.9991353,0.000002066434,0.0001935684,0.0002746938,0.0002575691,0.0001368089],"domain_scores_gemma":[0.9990941,0.00001053256,0.0001872273,0.000509407,0.0001544053,0.00004433882],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00000252429,0.00006744034,0.001793279,0.00003598198,0.000007845022,1.129912e-7,0.000182067,0.0000010109,0.5689901,0.4119978,0.008593479,0.008328416],"study_design_scores_gemma":[0.0005003724,0.00004339463,0.01008165,0.00009178728,0.00001102257,0.00004115673,0.00005505612,0.005637742,0.8418312,0.006781168,0.1347024,0.0002230271],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8962587,0.0001609026,0.02379195,0.01195324,0.002896202,0.001500391,0.00006310275,0.0003781162,0.06299744],"genre_scores_gemma":[0.9921904,0.000004319895,0.007260357,0.0001123555,0.00005853337,0.00004059914,0.000001144497,0.000003789894,0.0003284843],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4052166,"threshold_uncertainty_score":0.22574,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009657613492116489,"score_gpt":0.2334682218386412,"score_spread":0.2238106083465247,"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."}}