{"id":"W6998689723","doi":"","title":"An Anomaly Detection System for Smart Manufacturing Using Deep Learning","year":2021,"lang":"en","type":"article","venue":"Scholarship@Western (Western University)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"National Research Council Canada","keywords":"Anomaly detection; Autoencoder; Deep learning; Residual; Convolutional neural network; Thresholding; Focus (optics); Anomaly (physics)","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"],"consensus_categories":[],"category_scores_codex":[0.0002853462,0.0002303976,0.0002296528,0.0003286554,0.0008347938,0.00058526,0.0007899799,0.0001836146,0.000003316565],"category_scores_gemma":[0.00001162242,0.000290566,0.0001567769,0.0005014287,0.00003374039,0.002293307,0.0002814009,0.000358561,0.00002230444],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003655155,"about_ca_system_score_gemma":0.00006948764,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007262681,"about_ca_topic_score_gemma":0.0004128367,"domain_scores_codex":[0.9982021,0.0002113435,0.0002369353,0.0007449204,0.0002143286,0.0003903544],"domain_scores_gemma":[0.9986097,0.00005947677,0.0002054419,0.0006971734,0.0002222016,0.0002059664],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001140765,0.0003284382,0.6618468,0.0004322865,0.000197053,0.0004901474,0.0008032741,0.004005214,0.2651941,0.002880055,1.381116e-7,0.06370848],"study_design_scores_gemma":[0.0007047358,0.0002363944,0.1827795,0.000114572,0.00008116318,0.0003382851,0.0005991356,0.001523704,0.8098252,0.0001623279,0.002987666,0.0006473102],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5047102,0.00001577744,0.494484,0.00001697698,0.0001071052,0.0001496227,0.000002086977,0.0004758851,0.00003834059],"genre_scores_gemma":[0.9907057,0.000007375788,0.008434184,0.00005194875,0.00008700101,0.000006167045,0.000008960439,0.00003083994,0.00066784],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5446311,"threshold_uncertainty_score":0.9999546,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06768313866499705,"score_gpt":0.303259693765128,"score_spread":0.2355765551001309,"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."}}