{"id":"W2734701267","doi":"10.1002/aic.15860","title":"Design and assessment of delay timer alarm systems for nonlinear chemical processes","year":2017,"lang":"en","type":"article","venue":"AIChE Journal","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"ALARM; Manual fire alarm activation; Process (computing); Process state; Fault detection and isolation; Timer; Nonlinear system; Computer science; Chemical process; Benchmark (surveying); Constant false alarm rate; Engineering; Reliability engineering; Real-time computing; Algorithm; Artificial intelligence; Telecommunications","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003756636,0.00008508872,0.0001856104,0.00003213431,0.0001235931,0.0001423562,0.0001265212,0.00006601994,0.000004961943],"category_scores_gemma":[0.0000689917,0.00006930465,0.00003463141,0.00001923535,0.00002127062,0.0001318568,0.00001016311,0.0001453404,0.000001386236],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002676344,"about_ca_system_score_gemma":0.00004869636,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005386659,"about_ca_topic_score_gemma":7.169462e-7,"domain_scores_codex":[0.9994355,0.00001681056,0.0002317415,0.00006653645,0.0001206621,0.000128782],"domain_scores_gemma":[0.9994975,0.00005961053,0.0001240592,0.0001207099,0.0001241256,0.00007396896],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003963901,0.0003287469,0.006134645,0.005370451,0.002676009,0.00005534052,0.001215022,0.1256947,0.787989,0.000285496,0.02576796,0.0440862],"study_design_scores_gemma":[0.001217415,0.0001014605,0.0002187135,0.0001429079,0.00005995622,0.0003375474,0.0000717112,0.9665408,0.01356543,0.0000267895,0.01757318,0.0001440798],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2791434,0.006121115,0.7092702,0.0002509725,0.0019469,0.0009218378,0.00001978219,0.0001238494,0.002202024],"genre_scores_gemma":[0.9936177,0.0001891382,0.005731033,0.000006987009,0.00031029,0.00002476544,6.324265e-7,0.00001847835,0.0001009654],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8408461,"threshold_uncertainty_score":0.2826162,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02161494414173262,"score_gpt":0.2876765776120856,"score_spread":0.2660616334703531,"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."}}