{"id":"W2886613520","doi":"10.1108/jqme-04-2016-0017","title":"Remaining useful life estimation of metropolitan train wheels considering measurement error","year":2018,"lang":"en","type":"article","venue":"Journal of Quality in Maintenance Engineering","topic":"Railway Engineering and Dynamics","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Metropolitan area; Engineering; Prognostics; Estimation; Reliability engineering; Profiling (computer programming); Computer science","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":[0.0008240116,0.0005538836,0.0005602356,0.001240109,0.0002840837,0.0009018755,0.0008310031,0.0005590948,0.0007346324],"category_scores_gemma":[0.003247176,0.0002115236,0.0006335474,0.0006037557,0.0003267655,0.001087922,0.0007726199,0.0003876723,0.0001631708],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008293632,"about_ca_system_score_gemma":0.0007338616,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01216989,"about_ca_topic_score_gemma":0.00932533,"domain_scores_codex":[0.9994986,0.0000900105,0.00003975567,0.0001114383,0.0001997193,0.00006041492],"domain_scores_gemma":[0.9987195,0.0004410386,0.0002801359,0.0001554593,0.0003632839,0.00004067657],"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":[0.0001676994,0.00006576788,0.05081671,0.0002030453,0.00009167499,0.0002948517,0.0002490326,0.8161306,0.01181504,0.003142147,0.0005747712,0.1164487],"study_design_scores_gemma":[0.000002190439,0.0000370396,0.006432416,0.00001709889,0.00002186147,0.0000568916,0.00005377043,0.9896351,0.002338955,0.0009655163,0.0004247217,0.00001446513],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2251628,0.0006115872,0.7718329,0.0001211871,0.00002789449,0.00003406072,0.0001687974,0.000300671,0.001740076],"genre_scores_gemma":[0.9864928,0.0001419286,0.01240771,0.00001263926,0.000009971995,0.0000178826,0.0001388413,0.00001303724,0.0007652389],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01216989,"threshold_uncertainty_score":0.02419811,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04800241438323642,"score_gpt":0.2843186362590008,"score_spread":0.2363162218757644,"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."}}