{"id":"W3171436672","doi":"10.1002/int.22493","title":"Digital‐twin assisted: Fault diagnosis using deep transfer learning for machining tool condition","year":2021,"lang":"en","type":"article","venue":"International Journal of Intelligent Systems","topic":"Advanced machining processes and optimization","field":"Engineering","cited_by":104,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Automation; Cloud computing; Computer science; Process (computing); Fault (geology); Software deployment; Manufacturing engineering; Machining; Systems engineering; Engineering; Software engineering; Mechanical 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":[0.0001862624,0.0001490894,0.0002368898,0.0001733602,0.00006458815,0.000304995,0.0001787446,0.00007465263,0.00004591778],"category_scores_gemma":[0.0002751768,0.0001478555,0.0001712813,0.0001050919,0.00001316693,0.0005863186,0.00001458291,0.0002217644,0.00000390715],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002216766,"about_ca_system_score_gemma":0.00004206907,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002322528,"about_ca_topic_score_gemma":0.000001733838,"domain_scores_codex":[0.9986315,0.00002651192,0.0007037703,0.0001224248,0.0003636826,0.0001521466],"domain_scores_gemma":[0.9986349,0.000219881,0.0001631494,0.00005898484,0.0008654101,0.0000576861],"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":[0.00002930782,0.00003437821,0.001193454,0.00008640841,0.0002600938,0.00003949549,0.0002471107,0.9640624,0.001023766,0.0004023914,0.00004228377,0.03257891],"study_design_scores_gemma":[0.0006284188,0.00008511831,0.00008785063,0.0008463633,0.0000730959,0.0007640431,0.001176804,0.9580883,0.01174976,0.0001588271,0.02605811,0.0002832672],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06446365,0.001760067,0.9305583,0.00003841149,0.002605671,0.0000985939,0.00002577487,0.00004817801,0.0004013754],"genre_scores_gemma":[0.9949397,0.0004875722,0.003754748,0.00002684491,0.0005191157,0.00001509964,0.00008179111,0.00004353147,0.0001316223],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.930476,"threshold_uncertainty_score":0.6029375,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02158188550714101,"score_gpt":0.2891521106892455,"score_spread":0.2675702251821044,"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."}}