{"id":"W2033187065","doi":"10.3141/1866-02","title":"Development of Preventive Maintenance Decision Trees Based on Cost-Effectiveness Analysis: An Ontario Case Study","year":2004,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Ministère des Transports; University of Waterloo","keywords":"Christian ministry; Transport engineering; Cost–benefit analysis; Decision tree; Preventive maintenance; Cost analysis; Operations research; Computer science; Operations management; Risk analysis (engineering); Engineering; Business; Reliability engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004467897,0.0002644532,0.0005560606,0.001527024,0.0004163632,0.00006696062,0.0006231678,0.0001247452,0.00005839074],"category_scores_gemma":[0.00006363759,0.0001944548,0.0003462634,0.002328288,0.0001917825,0.0004532299,0.000004191095,0.001554627,0.000002902264],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001059735,"about_ca_system_score_gemma":0.0008954523,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.02449317,"about_ca_topic_score_gemma":0.6989847,"domain_scores_codex":[0.9945456,0.0005806627,0.001272518,0.000346427,0.002609749,0.0006450186],"domain_scores_gemma":[0.996192,0.0005594653,0.0002539384,0.0004763834,0.002248592,0.0002695789],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0021012,0.0007018167,0.2274478,0.0001403297,0.0006951717,0.001169365,0.01757744,0.7336565,0.001886335,0.0001175278,0.00002601697,0.0144805],"study_design_scores_gemma":[0.003018924,0.001009323,0.9781858,0.0006180905,0.0001809598,0.000002011704,0.009273379,0.0008404416,0.005858439,0.0004297157,0.0003836139,0.000199278],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9554793,0.00001827298,0.04258576,0.00002558931,0.000361109,0.001446776,0.0000209742,0.00002442142,0.00003782317],"genre_scores_gemma":[0.9892197,0.00001892118,0.01048967,0.000003915497,0.00005254211,0.0001337827,0.00001255342,0.00004204283,0.00002692984],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7507381,"threshold_uncertainty_score":0.9820028,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06357564386974288,"score_gpt":0.3777958133037653,"score_spread":0.3142201694340224,"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."}}