{"id":"W2789652994","doi":"10.1109/tie.2018.2795573","title":"Detection of Frequent Alarm Patterns in Industrial Alarm Floods Using Itemset Mining Methods","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Electronics","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":86,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"ALARM; Computer science; Data mining; Manual fire alarm activation; Visualization; Identification (biology); Real-time computing; 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":[0.001479722,0.0007498233,0.0009455699,0.005417244,0.0005019256,0.001227873,0.000975099,0.0007707576,0.0007261837],"category_scores_gemma":[0.005602394,0.0002728067,0.0008545654,0.003777358,0.0002778529,0.001096327,0.0007397932,0.0005860609,0.0003699032],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002602579,"about_ca_system_score_gemma":0.0004277631,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001271524,"about_ca_topic_score_gemma":0.001681615,"domain_scores_codex":[0.9987662,0.0002307639,0.0003005277,0.0002797256,0.0003223884,0.0001004547],"domain_scores_gemma":[0.9950015,0.003012106,0.0009009467,0.0003664469,0.0005710676,0.0001478363],"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":[0.001103393,0.0009510157,0.1875801,0.0009534067,0.0006440401,0.003952564,0.001381717,0.09335713,0.03339851,0.00415639,0.005756895,0.6667647],"study_design_scores_gemma":[0.0000608213,0.0005143834,0.06605548,0.0001528046,0.0002319368,0.00263392,0.0009862353,0.8953106,0.01975866,0.008951859,0.005243134,0.0001002463],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4054949,0.001125212,0.586392,0.000333614,0.00009139901,0.0002752837,0.003221127,0.001638538,0.001427853],"genre_scores_gemma":[0.7571284,0.0004199032,0.2384989,0.00005207151,0.00005507747,0.0002056173,0.002969024,0.00004515142,0.0006258004],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005417244,"threshold_uncertainty_score":0.007825553,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08564844060532585,"score_gpt":0.3378349914421768,"score_spread":0.252186550836851,"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."}}