{"id":"W2938098978","doi":"10.1007/s40534-019-0187-0","title":"Event management architecture for the monitoring and diagnosis of a fleet of trains: a case study","year":2019,"lang":"en","type":"article","venue":"Journal of Modern Transportation","topic":"Software System Performance and Reliability","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Bombardier (Canada)","funders":"Centre National de la Recherche Scientifique","keywords":"Train; Architecture; Event (particle physics); Key (lock); Fleet management; Asynchronous communication; Computer science; Systems architecture; Systems engineering; Event management; Operations research; Management system; Engineering; Telecommunications; Operations management; Computer security","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":[0.001251704,0.000420992,0.0003161827,0.0005806598,0.0006480733,0.001059654,0.0009986588,0.001464613,0.001422273],"category_scores_gemma":[0.002319206,0.0001778314,0.0004046624,0.0006166215,0.0006334145,0.001045038,0.0007544865,0.000680955,0.000182562],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001207347,"about_ca_system_score_gemma":0.0006858667,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006996103,"about_ca_topic_score_gemma":0.00548373,"domain_scores_codex":[0.9991026,0.0003821043,0.00006737994,0.0001347702,0.0002021716,0.0001110287],"domain_scores_gemma":[0.9987317,0.0007447115,0.00009497671,0.0001533031,0.0001724344,0.0001029517],"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":[0.001503414,0.001501602,0.04432039,0.0007651253,0.0001599411,0.01027629,0.003529303,0.6902339,0.03807482,0.02587732,0.004341235,0.1794167],"study_design_scores_gemma":[0.0001469897,0.0006605738,0.01074142,0.00004430761,0.00007177213,0.001156127,0.001661566,0.9498476,0.02042463,0.003445471,0.01176153,0.00003796707],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8130966,0.0002885417,0.1769499,0.0006693292,0.00004568628,0.0005052662,0.0003587574,0.0007823695,0.007303566],"genre_scores_gemma":[0.9523275,0.0001591206,0.0449902,0.00003345978,0.00001394976,0.0001060935,0.0002081354,0.00001753753,0.00214406],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006996103,"threshold_uncertainty_score":0.01391077,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01499920374885197,"score_gpt":0.2701996423248611,"score_spread":0.2552004385760091,"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."}}