{"id":"W2037978336","doi":"10.1109/acc.2010.5530508","title":"Correlation analysis of alarm data and alarm limit design for industrial processes","year":2010,"lang":"en","type":"article","venue":"","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":49,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Suncor Energy Incorporated","keywords":"ALARM; Computer science; Rationalization (economics); Data mining; Correlation; Process (computing); False alarm; Constant false alarm rate; Similarity (geometry); Artificial intelligence; Pattern recognition (psychology); Mathematics; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.003124601,0.0007357074,0.0008317735,0.002771465,0.0004783235,0.0008910859,0.0006942006,0.0005711733,0.0008875334],"category_scores_gemma":[0.02006804,0.0005395133,0.0005674541,0.001541307,0.0008912362,0.00118334,0.0007252628,0.0006016681,0.0001309866],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009619907,"about_ca_system_score_gemma":0.001558805,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002633582,"about_ca_topic_score_gemma":0.002209981,"domain_scores_codex":[0.9974713,0.001014107,0.0001808583,0.0002996621,0.0008767556,0.0001573897],"domain_scores_gemma":[0.989117,0.00714648,0.001440293,0.0006046295,0.001538374,0.0001532714],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0008411502,0.0001432377,0.01212646,0.0002157298,0.0001493701,0.0003094717,0.0002490121,0.7898671,0.009011175,0.02745815,0.0007584781,0.1588707],"study_design_scores_gemma":[0.00001873081,0.0001189997,0.003185673,0.00001107125,0.00003101988,0.00005878762,0.00002689682,0.9818785,0.005418407,0.008867047,0.0003634459,0.00002146967],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07272527,0.00009645656,0.925864,0.00007218671,0.000009890716,0.00004897961,0.00006193481,0.0004295555,0.0006917576],"genre_scores_gemma":[0.8918815,0.00009917635,0.1074422,0.00002483415,0.00001964077,0.0000833074,0.0001281823,0.00004479156,0.0002763493],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003124601,"threshold_uncertainty_score":0.01652461,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05204334277185933,"score_gpt":0.255842442701233,"score_spread":0.2037990999293736,"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."}}