{"id":"W4403714792","doi":"10.1016/j.conengprac.2024.106130","title":"Hierarchical grouping and visualization of correlated alarms using time-augmented word embedding","year":2024,"lang":"en","type":"article","venue":"Control Engineering Practice","topic":"Software System Performance and Reliability","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Visualization; Word (group theory); Computer science; Word embedding; Embedding; Artificial intelligence; Natural language processing; Pattern recognition (psychology); Speech recognition; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006370337,0.001344731,0.0005788393,0.004167657,0.0004571124,0.00142321,0.0007608762,0.0005989105,0.00252807],"category_scores_gemma":[0.003509777,0.0003355284,0.000815746,0.002756184,0.0004532244,0.001330783,0.00150856,0.0006910276,0.0008675274],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004357807,"about_ca_system_score_gemma":0.0006881406,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005118447,"about_ca_topic_score_gemma":0.0077847,"domain_scores_codex":[0.9993989,0.0001584884,0.00006004023,0.0001711273,0.0001519007,0.00005954062],"domain_scores_gemma":[0.9983603,0.0007165046,0.0002343388,0.0001851859,0.0004449086,0.00005878501],"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":[0.001017885,0.0003424947,0.01314602,0.000803879,0.0001684265,0.001041909,0.003349448,0.12303,0.07832439,0.01282611,0.01565092,0.7502984],"study_design_scores_gemma":[0.00005020133,0.0001878007,0.008239349,0.00005400897,0.0000638814,0.0003211876,0.0009308218,0.9397683,0.02563522,0.01466823,0.009993668,0.00008722689],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1473829,0.0005826474,0.8352422,0.000386288,0.0001408859,0.0002848124,0.002701029,0.01045054,0.002828766],"genre_scores_gemma":[0.4107593,0.0003112947,0.582997,0.00005210881,0.00004198641,0.0001919816,0.003792471,0.0003685195,0.001485338],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005118447,"threshold_uncertainty_score":0.01017731,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006675733171036756,"score_gpt":0.2670445899898179,"score_spread":0.2603688568187811,"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."}}