{"id":"W2153313896","doi":"10.1609/aimag.v34i1.2435","title":"Statistical Anomaly Detection for Train Fleets","year":2013,"lang":"en","type":"article","venue":"AI Magazine","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Bombardier (Canada)","funders":"","keywords":"Anomaly detection; Train; Anomaly (physics); Computer science; Bayesian probability; Data mining; Parametric statistics; Event (particle physics); Artificial intelligence; Statistics; Mathematics; Geography","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":[0.00009128759,0.00008451101,0.00008752799,0.00006059691,0.0001181373,0.0001129285,0.0002529229,0.00004929463,0.0001089423],"category_scores_gemma":[0.00002306437,0.00007922197,0.00004192763,0.0001983785,0.00002919016,0.000302075,0.00004735584,0.00007019124,0.0004452781],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000239969,"about_ca_system_score_gemma":0.00001660344,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003193738,"about_ca_topic_score_gemma":0.00001614759,"domain_scores_codex":[0.999303,0.00001451221,0.0001605703,0.0002534681,0.00008486886,0.0001835914],"domain_scores_gemma":[0.9994249,0.00006328354,0.0000411151,0.000292399,0.0001027222,0.00007556436],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00000406351,0.00007735909,0.00005972277,0.0000137778,0.000009919615,9.037367e-7,0.00004641088,0.000006168111,0.05013981,0.1517588,0.03477642,0.7631066],"study_design_scores_gemma":[0.0007780872,0.0008573848,0.0460947,0.000009582578,0.00001834285,0.00006275446,0.00001278495,0.3091509,0.07259063,0.1784779,0.3913655,0.0005813723],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003433598,0.000009381124,0.99137,0.002591773,0.00007199323,0.0004734677,0.000007734003,0.0003766118,0.00166539],"genre_scores_gemma":[0.829542,0.000002469552,0.1678299,0.0008209721,0.00006766643,0.0005709376,0.000004978941,0.000008617819,0.001152513],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8261083,"threshold_uncertainty_score":0.5723297,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009203911163737468,"score_gpt":0.2552243410683807,"score_spread":0.2460204299046432,"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."}}