{"id":"W4414801884","doi":"10.1016/j.jprocont.2025.103563","title":"Real-time identification of most critical alarms for alarm flood reduction","year":2025,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"ALARM; Identification (biology); Process (computing); Construct (python library); Prioritization; Flood myth; Markov process; False alarm","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":[0.0004213525,0.0007466531,0.0006260999,0.001420897,0.0004585034,0.0007087323,0.0004259926,0.0004778545,0.001478727],"category_scores_gemma":[0.002274198,0.0001864735,0.0001847481,0.0004042431,0.0001468305,0.0006405138,0.0003945543,0.0007584756,0.0005675547],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002118413,"about_ca_system_score_gemma":0.0005791186,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007096521,"about_ca_topic_score_gemma":0.00118026,"domain_scores_codex":[0.9997543,0.00003414893,0.00001682028,0.00006120251,0.00009323649,0.00004041375],"domain_scores_gemma":[0.9988035,0.0004696612,0.0002027475,0.00006515485,0.0003518025,0.0001071017],"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":[0.004275632,0.0006414503,0.03789786,0.0003382198,0.0001157591,0.0006921123,0.0003943642,0.05649198,0.2808133,0.002899761,0.01026251,0.605177],"study_design_scores_gemma":[0.00004497237,0.0005961056,0.02773731,0.00002796366,0.00007678094,0.0005123244,0.0001877132,0.9048337,0.06059168,0.002766034,0.002581945,0.00004340366],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4445017,0.0008118437,0.5459287,0.0004302674,0.0003442625,0.0001323757,0.0005492753,0.00399308,0.003308448],"genre_scores_gemma":[0.9599898,0.00008616377,0.03882378,0.00004078419,0.00005638722,0.00002229397,0.0002259556,0.0000376691,0.0007172372],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001478727,"threshold_uncertainty_score":0.004946828,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005979597824227073,"score_gpt":0.3027047610601266,"score_spread":0.2967251632358995,"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."}}