{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002911676,0.0006289293,0.0003781554,0.0005994307,0.0002132356,0.000439788,0.0008119959,0.0007422072,0.001528512],"category_scores_gemma":[0.0009174974,0.000159947,0.0003169505,0.0003746236,0.0002955032,0.0008531953,0.0006189363,0.0006731328,0.0003273895],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005240081,"about_ca_system_score_gemma":0.0005010859,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003314272,"about_ca_topic_score_gemma":0.003461507,"domain_scores_codex":[0.9998104,0.00002000111,0.000009955357,0.00005346634,0.00006573037,0.00004045273],"domain_scores_gemma":[0.9997638,0.00006646854,0.00003711649,0.00003034133,0.00008435047,0.00001800421],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004649058,0.0003757529,0.007558922,0.0001501001,0.00008706662,0.0003316881,0.0001187678,0.2897392,0.03084764,0.001824015,0.003254798,0.6652471],"study_design_scores_gemma":[0.000004986025,0.00004678711,0.0008542311,0.000003655288,0.000007342834,0.00004404264,0.00001038873,0.9920413,0.005747517,0.0009317062,0.0003017744,0.000006263324],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1402433,0.0005418485,0.8531823,0.0002740069,0.0001099674,0.00005784558,0.0001562903,0.003048861,0.002385629],"genre_scores_gemma":[0.9558156,0.00009442006,0.04204142,0.00008303452,0.00002121755,0.00002651694,0.0001553599,0.00002673465,0.001735844],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003314272,"threshold_uncertainty_score":0.006589949,"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."}}