{"id":"W1993186055","doi":"10.1109/tase.2011.2176490","title":"Performance Assessment and Design for Univariate Alarm Systems Based on FAR, MAR, and AAD","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Automation Science and Engineering","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":116,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Univariate; ALARM; Computation; Constant false alarm rate; Computer science; False alarm; Reliability engineering; Real-time computing; Variable (mathematics); Engineering; Algorithm; Artificial intelligence; Machine learning; Multivariate statistics; Mathematics","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":[0.001759061,0.0001253164,0.0001461239,0.0003990401,0.0004077075,0.0002320953,0.0001499501,0.00003808899,0.000005107199],"category_scores_gemma":[0.0001452968,0.0001036448,0.00001282696,0.0005348158,0.000121339,0.000794281,0.000002483386,0.0001001304,0.000002477522],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007146675,"about_ca_system_score_gemma":0.0000709656,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005690813,"about_ca_topic_score_gemma":3.589538e-7,"domain_scores_codex":[0.9984814,0.0000219983,0.0002690358,0.000378018,0.0006343773,0.0002151852],"domain_scores_gemma":[0.998708,0.0007208881,0.00006649314,0.0001700397,0.0001965006,0.0001381497],"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":[0.00004215196,0.00004257635,0.0001414944,0.00007715786,0.000006043704,0.000001253481,0.0004767982,0.8431898,0.004874495,0.001422406,0.000006209514,0.1497196],"study_design_scores_gemma":[0.0002602456,0.0001736119,0.009787955,0.00005068594,0.000007947357,0.000003973596,0.0001096917,0.9865546,0.002694344,0.0001797471,0.00005433751,0.0001228908],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04445184,0.00001398148,0.9546107,0.0000343565,0.0004195169,0.0002643008,0.000008409268,0.00007981432,0.000117056],"genre_scores_gemma":[0.918978,0.00001984983,0.08087625,0.00002109009,0.000009821175,0.00005883428,1.334664e-7,0.000008231603,0.00002781787],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8745261,"threshold_uncertainty_score":0.4226512,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1142831347647703,"score_gpt":0.3553357846621226,"score_spread":0.2410526498973523,"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."}}