{"id":"W1966782165","doi":"10.1021/ie202880w","title":"Dynamic Risk Assessment and Fault Detection Using Principal Component Analysis","year":2012,"lang":"en","type":"article","venue":"Industrial & Engineering Chemistry Research","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":81,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Computer science; Principal component analysis; Reliability engineering; Fault detection and isolation; Fault (geology); Process (computing); Component (thermodynamics); Risk analysis (engineering); Warning system; Risk assessment; Data mining; Artificial intelligence; Engineering; Computer security","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":[0.00147082,0.00154701,0.001112747,0.004068329,0.000508312,0.001791343,0.001071668,0.0007992926,0.001148948],"category_scores_gemma":[0.004308993,0.0004772812,0.001009657,0.001675748,0.0006310672,0.001791478,0.001115541,0.001024017,0.0004700603],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006198209,"about_ca_system_score_gemma":0.001024434,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002192285,"about_ca_topic_score_gemma":0.001211579,"domain_scores_codex":[0.9981403,0.0004370141,0.0001084929,0.0003160539,0.0009044665,0.00009361978],"domain_scores_gemma":[0.9981133,0.0008629851,0.0003188037,0.0001841088,0.0004684028,0.0000523992],"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":[0.0001238871,0.0001991726,0.006389083,0.0002412938,0.0002371121,0.0002347599,0.0002453779,0.3706197,0.02123098,0.02443434,0.001522316,0.5745221],"study_design_scores_gemma":[0.00001308497,0.0001291292,0.002609831,0.00002453887,0.00004397473,0.0001808843,0.00005335297,0.9647452,0.008039944,0.02100094,0.003085349,0.00007383234],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005250291,0.0001214628,0.993715,0.0000456621,0.00001206719,0.00003423644,0.00002957386,0.0003683229,0.0004233902],"genre_scores_gemma":[0.3199076,0.0004263601,0.6775295,0.00003994219,0.00006238253,0.0001833138,0.0002378151,0.0001272179,0.001485875],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004068329,"threshold_uncertainty_score":0.007778525,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05427607355759193,"score_gpt":0.3421849376085765,"score_spread":0.2879088640509846,"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."}}