{"id":"W2310024467","doi":"10.1139/cjce-2014-0500","title":"Prediction of maintenance cost for road construction equipment: a case study","year":2016,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Quality and Safety in Healthcare","field":"Health Professions","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Natural Resources","funders":"Natural Sciences and Engineering Research Council of Canada; McGill University","keywords":"Data collection; Predictive maintenance; Cost estimate; Cost database; Reliability engineering; Engineering; Preventive maintenance; Computer science; Regression analysis; Work (physics); Operations research; Systems engineering; Statistics; Database; Machine learning","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.001114655,0.00008830204,0.0002441078,0.0002388566,0.0002187948,0.000003436998,0.00008741456,0.000098964,0.000122181],"category_scores_gemma":[0.0006330027,0.00006854084,0.00006590427,0.0001144239,0.00003313576,0.0001515591,0.000006477536,0.0002463301,0.000001909777],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004823304,"about_ca_system_score_gemma":0.001134273,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004415519,"about_ca_topic_score_gemma":0.1853419,"domain_scores_codex":[0.9985678,0.0001048089,0.000769563,0.00008519532,0.00011354,0.000359061],"domain_scores_gemma":[0.9983444,0.0003090065,0.0003236837,0.0001295864,0.0004676616,0.0004257018],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"qualitative","study_design_scores_codex":[0.0007360704,0.000227683,0.6954002,0.006527044,0.001087683,0.004631554,0.09385212,0.009919452,0.003044138,0.02081304,0.01685141,0.1469097],"study_design_scores_gemma":[0.04551363,0.0085618,0.2808547,0.03155719,0.0008437926,0.02040927,0.3306711,0.01435318,0.0004262082,0.003682954,0.2610529,0.002073244],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9103773,0.0003303822,0.08000348,0.00205798,0.004677063,0.001623885,0.0005284456,0.00002401555,0.0003774251],"genre_scores_gemma":[0.9992164,0.00001874611,0.0002605566,0.00004404447,0.0003380366,0.00003503394,0.000001121867,0.00001535947,0.00007071261],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4145455,"threshold_uncertainty_score":0.8295235,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08715721784869224,"score_gpt":0.3527827640035037,"score_spread":0.2656255461548114,"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."}}