{"id":"W2764262197","doi":"10.1007/978-3-319-69084-1_6","title":"Self-tuning Eventually-Consistent Data Stores","year":2017,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Distributed computing; Latency (audio); Replication (statistics); Cloud computing; Convergence (economics); Consistency (knowledge bases); Data center; Distributed data store; Focus (optics); Data consistency; Distributed database; Computer network; Artificial intelligence; Operating system; 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":["metaepi_narrow","scholarly_communication","open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.001989015,0.0006613826,0.0006493904,0.0007238099,0.0009160306,0.001586196,0.01654306,0.0002766825,0.000009998335],"category_scores_gemma":[0.0001693356,0.0005901766,0.0001568449,0.0002129964,0.0006079749,0.0002409254,0.01552062,0.0009633881,0.00007360506],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003292138,"about_ca_system_score_gemma":0.0005651421,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004243488,"about_ca_topic_score_gemma":0.00009201479,"domain_scores_codex":[0.9942581,0.00005728295,0.0006036811,0.002705379,0.001476397,0.0008991721],"domain_scores_gemma":[0.9911779,0.0004293156,0.0006005742,0.007357813,0.000186935,0.0002475231],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000003094986,0.00005884304,0.00005049849,0.00009275042,0.00006151125,0.0002900042,0.0009614839,0.02705737,0.000007811243,0.01349046,0.0002350815,0.9576911],"study_design_scores_gemma":[0.0002721922,0.0001111545,0.00009911507,0.0005857883,0.000024103,0.00006848331,2.720575e-7,0.9290437,0.00002956612,0.0274863,0.04150295,0.0007764191],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00008262105,0.0009909951,0.9791413,0.00146781,0.003010445,0.0004258581,0.000006472256,0.0004485237,0.01442595],"genre_scores_gemma":[0.1352709,0.00009480098,0.8565463,0.001969632,0.001974734,0.000009784638,0.00002229957,0.00009835916,0.004013096],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9569147,"threshold_uncertainty_score":0.9996549,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03522988097253758,"score_gpt":0.2633968243196291,"score_spread":0.2281669433470915,"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."}}