{"id":"W2622060889","doi":"10.1002/cjce.22916","title":"Diagnosis of sensor precision degradation using Kullback‐Leibler divergence","year":2017,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Fault detection and isolation; Divergence (linguistics); Principal component analysis; Fault (geology); Kullback–Leibler divergence; Degradation (telecommunications); Computer science; Continuous stirred-tank reactor; Variance (accounting); Data mining; Algorithm; Pattern recognition (psychology); Control theory (sociology); Engineering; Artificial intelligence; Control (management)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002071468,0.0009683971,0.001407765,0.001679569,0.0004968295,0.001645595,0.000966705,0.0009444175,0.0006582726],"category_scores_gemma":[0.007411958,0.0002814209,0.000597991,0.0008475024,0.001126958,0.001345817,0.001086789,0.001032271,0.0001822823],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001205473,"about_ca_system_score_gemma":0.001103051,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003929468,"about_ca_topic_score_gemma":0.00185092,"domain_scores_codex":[0.9982705,0.0002934544,0.0001879007,0.0003686557,0.0007555729,0.0001239423],"domain_scores_gemma":[0.9964808,0.001726913,0.0005968753,0.0001904939,0.0008735046,0.0001315284],"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":[0.0006095164,0.0001997818,0.01631363,0.0004559311,0.0002114179,0.0007718085,0.0002548815,0.6721709,0.02297013,0.009785018,0.002054587,0.2742024],"study_design_scores_gemma":[0.000007587438,0.00006982237,0.001460021,0.000007979545,0.00000953103,0.00009909914,0.00002114134,0.993108,0.002951659,0.002097238,0.0001483588,0.00001950014],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05803566,0.000392223,0.9398212,0.0001518811,0.00004281676,0.00004120004,0.00004966565,0.0004636588,0.001001697],"genre_scores_gemma":[0.9324026,0.0001849886,0.06650127,0.00005955005,0.00002600524,0.00004376522,0.00009256043,0.00002440007,0.0006649322],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003929468,"threshold_uncertainty_score":0.0109551,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0175522308957341,"score_gpt":0.2161048109376681,"score_spread":0.198552580041934,"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."}}