{"id":"W2966114666","doi":"10.1109/rams.2019.8769273","title":"Maintenance Effectiveness Estimation with Applications to Railway Industry","year":2019,"lang":"en","type":"article","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Estimation; Computer science; Maintenance engineering; Reliability engineering; Engineering; Systems engineering","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":[0.0001012507,0.00008056458,0.00008853002,0.00004025209,0.00002084132,0.00002021953,0.00006769308,0.00008292939,0.00008209575],"category_scores_gemma":[0.0000111069,0.00006375564,0.00001289382,0.0002528075,0.0000110464,0.0001392433,0.000009433731,0.0001186945,0.0003139215],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000724554,"about_ca_system_score_gemma":0.00001075059,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006888038,"about_ca_topic_score_gemma":0.000004454045,"domain_scores_codex":[0.9995701,0.000009452061,0.00008161094,0.0001367314,0.00007007118,0.0001320262],"domain_scores_gemma":[0.999639,0.00003716931,0.000009889901,0.000210198,0.00005484003,0.00004889728],"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":[0.0000113085,0.000008882842,0.001405868,0.00007358993,0.000006473028,1.257069e-7,0.00002580795,0.9868811,0.0007825618,0.004508485,0.0002063558,0.006089401],"study_design_scores_gemma":[0.0008663884,0.0001466487,0.03347966,0.0002280289,0.0000146905,0.000009524187,0.0001293282,0.9396632,0.01178643,0.001195932,0.01200783,0.0004722825],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09683947,0.000004588267,0.8812188,0.0000849795,0.00005627635,0.0008060908,0.000001907056,0.0002513476,0.02073658],"genre_scores_gemma":[0.9653077,0.00000283828,0.03358722,0.00006157623,0.00001255921,0.0002858295,0.000008796136,0.0000174354,0.0007161119],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8684682,"threshold_uncertainty_score":0.403493,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003893989824217334,"score_gpt":0.2065292641735177,"score_spread":0.2026352743493004,"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."}}