{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001066701,0.0009437838,0.0006803764,0.001415214,0.000667443,0.003567162,0.001751174,0.001773395,0.02021526],"category_scores_gemma":[0.005464302,0.0003755721,0.0006175007,0.001995772,0.001446621,0.006359134,0.003136906,0.002782182,0.006008153],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001126979,"about_ca_system_score_gemma":0.0008867275,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001476677,"about_ca_topic_score_gemma":0.00110552,"domain_scores_codex":[0.9991837,0.0001431947,0.00004445078,0.0001950946,0.0003808386,0.00005277151],"domain_scores_gemma":[0.9984471,0.0005682288,0.0001110647,0.0004775942,0.000295479,0.0001005417],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002469288,0.00006972668,0.001245541,0.0005227029,0.00005114568,0.000368604,0.0001652952,0.04085391,0.005483263,0.4945085,0.0428393,0.4136451],"study_design_scores_gemma":[0.00003708821,0.0001514331,0.0006202615,0.0003652956,0.00004944908,0.0006951815,0.000143642,0.2566486,0.007624474,0.3171805,0.4164095,0.00007460496],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006860967,0.01916604,0.8647775,0.004534177,0.003041715,0.0001402101,0.001200227,0.006728743,0.09355038],"genre_scores_gemma":[0.5155794,0.0323594,0.3833404,0.003132413,0.003652072,0.0004514595,0.00352109,0.001450655,0.05651315],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02021526,"threshold_uncertainty_score":0.06762671,"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."}}