{"id":"W2046474027","doi":"10.1007/s00778-013-0318-x","title":"Consistency anomalies in multi-tier architectures: automatic detection and prevention","year":2013,"lang":"en","type":"article","venue":"The VLDB Journal","topic":"Distributed systems and fault tolerance","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Consistency (knowledge bases); Computer science; Anomaly detection; Isolation (microbiology); Database transaction; Set (abstract data type); Data mining; Database; Distributed computing; Artificial intelligence; Programming language","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":[0.0004275525,0.0000798184,0.0001099955,0.00007852236,0.0001939051,0.0003108568,0.0002683119,0.00003240485,0.00001445094],"category_scores_gemma":[0.00003703727,0.00004903989,0.00003782412,0.0001628869,0.00004716187,0.0002468652,0.00005137547,0.0001996122,0.00001914175],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002459496,"about_ca_system_score_gemma":0.00002887141,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006092221,"about_ca_topic_score_gemma":0.00007023061,"domain_scores_codex":[0.9991673,0.000165647,0.000252153,0.0001111797,0.0001376748,0.0001661094],"domain_scores_gemma":[0.9995567,0.00004507844,0.0001274603,0.0001733386,0.00004587057,0.00005154127],"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":[0.000004641247,0.00008049011,0.003662102,0.00004163759,0.00003527185,0.00001696958,0.003931032,0.0004431538,0.004000664,0.0007856808,0.0002082087,0.9867901],"study_design_scores_gemma":[0.001198721,0.0001585163,0.4607226,0.0002606325,0.000009959444,0.001919555,0.0004971633,0.5142197,0.0004574899,0.01954499,0.0007758898,0.0002347926],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6651632,0.0005884957,0.3331377,0.0005188402,0.0002358361,0.0001763884,4.863302e-7,0.00002961126,0.0001494301],"genre_scores_gemma":[0.9939563,0.00001716323,0.00575299,0.00006243857,0.00003940168,0.00001578463,1.156885e-7,0.000003412987,0.0001523931],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9865553,"threshold_uncertainty_score":0.29976,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0173761358373862,"score_gpt":0.2477361317347878,"score_spread":0.2303599958974016,"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."}}