{"id":"W2946622927","doi":"10.1016/j.procs.2019.04.184","title":"IoT-based predictive maintenance for fleet management","year":2019,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Software System Performance and Reliability","field":"Computer Science","cited_by":99,"is_retracted":false,"has_abstract":true,"ca_institutions":"Wilfrid Laurier University; University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Predictive maintenance; Internet of Things; Architecture; Fleet management; Big data; Work (physics); Machine learning; Computer security; Data mining; Reliability engineering; Telecommunications","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002989182,0.0004177454,0.0004298064,0.0004879946,0.0003576291,0.000497451,0.0008241589,0.0004146059,0.001437462],"category_scores_gemma":[0.0006619703,0.0001671373,0.0002891661,0.0004189127,0.0002490919,0.0007575181,0.000457993,0.0003887159,0.0003104953],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004225802,"about_ca_system_score_gemma":0.0003551846,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002532282,"about_ca_topic_score_gemma":0.003607934,"domain_scores_codex":[0.9998289,0.00002636604,0.00001008922,0.00004590454,0.00006540262,0.00002323979],"domain_scores_gemma":[0.9997123,0.00007492376,0.00005436387,0.00005637798,0.00008066037,0.00002139173],"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":[0.000242683,0.0001461071,0.004908167,0.0001499569,0.00006300576,0.0002691417,0.0001286603,0.7140538,0.01627838,0.0059283,0.004994708,0.252837],"study_design_scores_gemma":[0.00000578297,0.0000344787,0.0009933846,0.00000681238,0.000009832829,0.00004349198,0.00001835972,0.9934768,0.001722724,0.002316085,0.001366305,0.000005909595],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1172984,0.0008828662,0.8688216,0.000421259,0.0001679978,0.000108184,0.0003505092,0.003549839,0.008399447],"genre_scores_gemma":[0.9622892,0.0001691849,0.03570255,0.00004570993,0.0000271936,0.00003840629,0.0002199257,0.00004447787,0.001463298],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002532282,"threshold_uncertainty_score":0.005035102,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005923858653000614,"score_gpt":0.2219247922871021,"score_spread":0.2160009336341015,"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."}}