{"id":"W4366977974","doi":"10.1117/12.2660270","title":"Digital twin for predictive maintenance","year":2023,"lang":"en","type":"article","venue":"","topic":"Digital Transformation in Industry","field":"Engineering","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Computer science; Predictive maintenance; Predictive analytics; Internet of Things; Data science; Data modeling; The Internet; Cyber-physical system; Industrial Internet; Analytics; Digital ecosystem; Big data; Distributed computing; Engineering; Data mining; Software engineering; Embedded system; World Wide Web; Reliability 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":[0.00002664035,0.00005684147,0.00004855737,0.00004364751,0.00001438766,0.00006048748,0.00006340459,0.0000377917,0.00002815767],"category_scores_gemma":[0.00001900548,0.00005280116,0.00003186234,0.0001603384,0.00001317251,0.0004766019,0.00000614504,0.00004245242,0.0004034922],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002187592,"about_ca_system_score_gemma":0.000004509617,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":1.050186e-7,"about_ca_topic_score_gemma":1.833979e-7,"domain_scores_codex":[0.9996284,4.817073e-7,0.0000974495,0.00005453401,0.00006102131,0.0001581265],"domain_scores_gemma":[0.999831,0.00004495223,0.000004000172,0.00006555263,0.00002015902,0.00003435426],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001782599,0.00001669897,0.0005592152,0.0001919612,0.00009234196,0.000004081855,0.0004135374,0.1397953,0.0001150135,0.01778645,0.7603723,0.08063525],"study_design_scores_gemma":[0.0008389435,0.00007918653,0.001771414,0.0000624891,0.000006389853,0.000006960915,0.001539766,0.3737415,0.004023042,0.006550979,0.6109912,0.0003880919],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.007683112,0.000002948482,0.1151066,0.0001022028,0.0003235003,0.0001968696,0.0002383163,0.001819461,0.874527],"genre_scores_gemma":[0.9843369,0.000003195421,0.000172627,0.00002324063,0.00005578949,0.00008582689,0.00005524224,0.00002123437,0.01524599],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9766538,"threshold_uncertainty_score":0.5186211,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0156843685365709,"score_gpt":0.2189293269860314,"score_spread":0.2032449584494605,"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."}}