{"id":"W2996310699","doi":"10.1109/snpd.2019.8935752","title":"Assets Predictive Maintenance Using Convolutional Neural Networks","year":2019,"lang":"en","type":"article","venue":"","topic":"Currency Recognition and Detection","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Convolutional neural network; Computer science; Support vector machine; Artificial intelligence; Perceptron; Predictive maintenance; Transformation (genetics); Pattern recognition (psychology); Random forest; Multilayer perceptron; Classifier (UML); Machine learning; Representation (politics); Data mining; Artificial neural network; 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":[0.00009175957,0.00006940393,0.0000703391,0.00004665658,0.00005869455,0.00006326725,0.0001653147,0.00003959062,0.00016999],"category_scores_gemma":[0.000009927456,0.00006110578,0.00004435403,0.0002035859,0.00001846627,0.000468439,0.00007624758,0.0001060421,0.0001060344],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004380812,"about_ca_system_score_gemma":0.00002642409,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001224298,"about_ca_topic_score_gemma":0.000002937076,"domain_scores_codex":[0.9993142,0.00003092765,0.0001100509,0.0002283484,0.0001427576,0.0001736882],"domain_scores_gemma":[0.9996141,0.00003770473,0.00004453542,0.0001558253,0.00009716444,0.00005069736],"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":[0.0002530171,0.0006043603,0.1269119,0.00006685487,0.0001879845,0.00004184373,0.0005550227,0.3381359,0.005470757,0.2604232,0.0212524,0.2460968],"study_design_scores_gemma":[0.0002290862,0.00004815033,0.0104797,0.00000803384,0.000001548171,0.00004519605,0.0000078567,0.987825,0.00007787418,0.0008803633,0.0003122896,0.00008486437],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06835659,0.00002673278,0.9237515,0.00008498387,0.001624258,0.0001137107,0.000001540931,0.0001579013,0.005882768],"genre_scores_gemma":[0.9950233,0.000002138602,0.004144108,0.0002811963,0.00007931841,0.000003303923,0.000002659967,0.000003025605,0.0004609684],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9266667,"threshold_uncertainty_score":0.2491821,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02025516740115408,"score_gpt":0.2445189863372457,"score_spread":0.2242638189360916,"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."}}