{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00143317,0.0005973109,0.0006126597,0.002581154,0.0004535984,0.0008516188,0.001219523,0.0006792241,0.001320975],"category_scores_gemma":[0.01009751,0.0003094492,0.0007198176,0.001614357,0.0006963143,0.001590947,0.0009547945,0.001205355,0.0004069511],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007741654,"about_ca_system_score_gemma":0.0007916699,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005487216,"about_ca_topic_score_gemma":0.003650287,"domain_scores_codex":[0.9982358,0.0003417821,0.0001124491,0.0002614047,0.000915374,0.0001331729],"domain_scores_gemma":[0.9957728,0.001985589,0.0004766998,0.0004689266,0.001168286,0.0001277089],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001864614,0.0001105248,0.01161192,0.0001009539,0.0001232428,0.000323901,0.0002667308,0.5434058,0.02856884,0.03338294,0.003371805,0.3785469],"study_design_scores_gemma":[0.000003760361,0.0000177417,0.0008253216,0.000002769444,0.000003971384,0.00008215361,0.00001219141,0.9873904,0.00272847,0.007821344,0.001101841,0.00001000535],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01842291,0.00005254495,0.9792212,0.00007042257,0.00001821959,0.00002027499,0.00008017239,0.001682215,0.0004320264],"genre_scores_gemma":[0.4865772,0.0001355499,0.5105,0.00005253624,0.0000511578,0.00009059452,0.0006006703,0.0002834113,0.001708956],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005487216,"threshold_uncertainty_score":0.01091051,"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."}}