{"id":"W1985410087","doi":"10.1016/j.nucengdes.2014.09.037","title":"Signal de-noising methods for fault diagnosis and troubleshooting at CANDU® stations","year":2014,"lang":"en","type":"article","venue":"Nuclear Engineering and Design","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Troubleshooting; Reliability engineering; Fault (geology); Process (computing); Noise (video); SIGNAL (programming language); Computer science; Fault detection and isolation; Data mining; Engineering; Real-time computing; Artificial intelligence","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":[0.0005590178,0.0005689245,0.0003508988,0.0008806277,0.0006044889,0.0006140946,0.001036155,0.0005053224,0.002529595],"category_scores_gemma":[0.001173285,0.00024423,0.0001857232,0.0005483613,0.0003218229,0.0004523251,0.0004127542,0.0008405244,0.0004280274],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006359939,"about_ca_system_score_gemma":0.0007109382,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003065194,"about_ca_topic_score_gemma":0.008790668,"domain_scores_codex":[0.999604,0.00004832708,0.00001827248,0.00009051447,0.0001996115,0.0000393119],"domain_scores_gemma":[0.9993019,0.0002438045,0.00009225052,0.00006928697,0.000250383,0.00004241321],"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":[0.001714343,0.0002330243,0.005784582,0.0003634301,0.00005037585,0.0000933972,0.0002401295,0.03578057,0.3659534,0.0019123,0.001554861,0.5863195],"study_design_scores_gemma":[0.0001464476,0.00083938,0.01965886,0.00005161448,0.0001119731,0.0002774409,0.0002146286,0.4391567,0.5269618,0.001523627,0.01097293,0.0000846703],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2439249,0.001405284,0.7446532,0.0002883665,0.0001943684,0.0001144502,0.0001904891,0.001820295,0.007408726],"genre_scores_gemma":[0.788175,0.0004052905,0.2012106,0.00008820777,0.00006382725,0.00006106794,0.0002073962,0.00008653461,0.009702052],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003065194,"threshold_uncertainty_score":0.00846231,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01747441023686677,"score_gpt":0.2544461571277992,"score_spread":0.2369717468909324,"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."}}