{"id":"W4304957914","doi":"10.36227/techrxiv.21301533.v1","title":"Transfer Learning-motivated Intelligent Fault Diagnosis Designs: A Survey, Insights, and Perspectives","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Transfer of learning; Artificial intelligence; Field (mathematics); Reliability (semiconductor); Automation; Knowledge transfer; Machine learning; Presentation (obstetrics); Knowledge management; Data science; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003578161,0.0004298367,0.000537706,0.0002753861,0.0001467925,0.00015585,0.0002178647,0.0002837654,0.001330363],"category_scores_gemma":[0.0001066347,0.0004047258,0.0001597115,0.0002430124,0.00004630894,0.0000849863,0.0001424921,0.00128396,0.00002447618],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002602712,"about_ca_system_score_gemma":0.00003180922,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00132317,"about_ca_topic_score_gemma":0.0005067765,"domain_scores_codex":[0.9979837,0.0005016684,0.0004116266,0.0005540674,0.0002758007,0.0002731579],"domain_scores_gemma":[0.9991904,0.0002804091,0.00002906902,0.0002839798,0.00008344348,0.0001326593],"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":[0.0001595168,0.0002581762,0.009318222,0.0008756124,0.002071917,0.00005116563,0.02652853,0.9222041,0.002164071,0.0009070527,0.002014413,0.0334472],"study_design_scores_gemma":[0.002631164,0.0008379722,0.03473723,0.0004797009,0.000324455,0.00004429126,0.04414541,0.6329761,0.01039133,0.0003899054,0.2685856,0.004456796],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9278958,0.02064407,0.03653613,0.0001822773,0.001910207,0.001255084,0.00008233271,0.002612694,0.008881433],"genre_scores_gemma":[0.9920223,0.005374314,0.00002938368,0.00002544983,0.00007556016,0.000751214,0.0000596985,0.000091332,0.001570758],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.289228,"threshold_uncertainty_score":0.9998404,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03888681663096527,"score_gpt":0.2459939402290182,"score_spread":0.207107123598053,"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."}}