{"id":"W4301753623","doi":"10.1609/aaai.v26i2.18961","title":"Statistical Anomaly Detection for Train Fleets","year":2012,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Software System Performance and Reliability","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Bombardier (Canada)","funders":"","keywords":"Anomaly detection; Train; Euros; Computer science; Anomaly (physics); Event (particle physics); Component (thermodynamics); Real-time computing; Data mining; 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":[0.001182968,0.0006222219,0.0006155575,0.00279128,0.0004184122,0.0007428087,0.001270577,0.0005993656,0.001457906],"category_scores_gemma":[0.007622382,0.0003204308,0.0006705706,0.00182177,0.0004697279,0.001344168,0.000793444,0.0009969629,0.0004148208],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006650279,"about_ca_system_score_gemma":0.0007067035,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006785817,"about_ca_topic_score_gemma":0.004837357,"domain_scores_codex":[0.9986821,0.0002195468,0.00009407497,0.0002457293,0.0006324698,0.000126097],"domain_scores_gemma":[0.9967374,0.001416027,0.0004412971,0.000421307,0.0008479375,0.0001360256],"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":[0.0002323838,0.0001399208,0.02432359,0.00009890696,0.0001567999,0.0003977697,0.0002380097,0.6303877,0.02688434,0.01592991,0.003730566,0.2974802],"study_design_scores_gemma":[0.000003525295,0.0000154401,0.001243508,0.000001938279,0.00000389824,0.00005938857,0.00000921844,0.9921038,0.002225332,0.00353228,0.000793583,0.000008172205],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05239141,0.00006869798,0.9423886,0.00008720757,0.0000303816,0.00003533208,0.0003148066,0.004114815,0.0005687607],"genre_scores_gemma":[0.63139,0.00009997803,0.3650927,0.00004019383,0.00005161724,0.0001002258,0.001364343,0.0003307087,0.001530171],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006785817,"threshold_uncertainty_score":0.01349264,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07159672911517209,"score_gpt":0.3064512555109514,"score_spread":0.2348545263957793,"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."}}