{"id":"W4391171477","doi":"10.1002/cjce.25181","title":"A robust neural network model for fault detection in the presence of mislabelled data","year":2024,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland; Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial neural network; Fault detection and isolation; Computer science; Fault (geology); Artificial intelligence; Data mining; Reliability engineering; Machine learning; Engineering; Seismology; Geology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.001760453,0.0008513308,0.0009512188,0.000627702,0.0002762951,0.001112115,0.001811974,0.001693285,0.001438113],"category_scores_gemma":[0.005192698,0.0004023143,0.0006357242,0.000564435,0.0007313235,0.001072752,0.0006404235,0.001314475,0.0003093598],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001276609,"about_ca_system_score_gemma":0.0009395987,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01525818,"about_ca_topic_score_gemma":0.008963591,"domain_scores_codex":[0.9992015,0.0001862852,0.00005182019,0.0002652458,0.0002114428,0.00008375546],"domain_scores_gemma":[0.9981517,0.0009606557,0.0002686664,0.0000950666,0.000493757,0.00003002018],"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":[0.00004953276,0.00001573874,0.0004082086,0.00002767461,0.00002237237,0.00003949849,0.00001997761,0.9900816,0.0008727465,0.001140235,0.0001083821,0.007214033],"study_design_scores_gemma":[0.000001138883,0.00000558525,0.00005838114,0.000001695394,0.000002458761,0.0000027419,7.897875e-7,0.9995638,0.0001217118,0.0002196647,0.00002063655,0.000001472838],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07989929,0.0005166896,0.9157198,0.0003623415,0.00007562331,0.00007650496,0.000286757,0.0005587338,0.002504169],"genre_scores_gemma":[0.9593928,0.0001884874,0.03659738,0.00007134015,0.00002831959,0.0001534047,0.0002062102,0.00002906459,0.003333044],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01525818,"threshold_uncertainty_score":0.0303387,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0249778154350024,"score_gpt":0.2107290048664499,"score_spread":0.1857511894314475,"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."}}