{"id":"W3137727436","doi":"10.36001/ijphm.2014.v5i2.2238","title":"Predictive Model Evaluation for PHM","year":2020,"lang":"en","type":"article","venue":"International Journal of Prognostics and Health Management","topic":"Efficiency Analysis Using DEA","field":"Decision Sciences","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke; Carleton University; National Research Council Canada","funders":"Instituto de Ciencias del Mar y Limnología, Universidad Nacional Autónoma de México; National Natural Science Foundation of China; University of Cambridge","keywords":"Computer science; Machine learning; Predictive modelling; Artificial intelligence; Model validation; Domain (mathematical analysis); Reliability engineering; Engineering; Data 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":[0.008011096,0.001073689,0.001286282,0.002490217,0.000492809,0.002029341,0.001166074,0.0008474636,0.002468612],"category_scores_gemma":[0.02222385,0.0002406348,0.0007983008,0.00194689,0.0005484392,0.001615923,0.001518793,0.001460652,0.000402938],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001601269,"about_ca_system_score_gemma":0.001471376,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003545408,"about_ca_topic_score_gemma":0.002396855,"domain_scores_codex":[0.9961591,0.001792642,0.0003132546,0.0003851358,0.0011927,0.0001571515],"domain_scores_gemma":[0.9912675,0.005473854,0.0005957459,0.0008172529,0.001749759,0.00009593939],"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":[0.0001353928,0.0001168791,0.00567292,0.0006595809,0.0001615597,0.0001816276,0.0001439661,0.7033108,0.002618626,0.07373397,0.004153269,0.2091114],"study_design_scores_gemma":[0.000004876545,0.0000388272,0.0006985438,0.00005286223,0.0000229072,0.00004238879,0.000040431,0.9792077,0.001619582,0.01560931,0.002651781,0.00001076189],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01679581,0.001005052,0.9766421,0.000300447,0.00005933504,0.0001578459,0.0004001334,0.0004753064,0.004163857],"genre_scores_gemma":[0.6010071,0.001761701,0.3920458,0.0001897248,0.0001083129,0.0005367865,0.001927955,0.0001791748,0.002243415],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008011096,"threshold_uncertainty_score":0.04236728,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3460954191233478,"score_gpt":0.5115522055623375,"score_spread":0.1654567864389896,"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."}}