{"id":"W2468396395","doi":"10.1016/j.conengprac.2016.05.021","title":"A local alignment approach to similarity analysis of industrial alarm flood sequences","year":2016,"lang":"en","type":"article","venue":"Control Engineering Practice","topic":"Data Stream Mining Techniques","field":"Computer Science","cited_by":77,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"ALARM; Computation; Computer science; Flood myth; Smith–Waterman algorithm; Similarity (geometry); Data mining; Matching (statistics); Algorithm; Artificial intelligence; Engineering; Sequence alignment; Mathematics; Geography","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":[0.001108206,0.0001766353,0.0003805822,0.0003690916,0.00002924139,0.00007957059,0.001024763,0.0001064474,0.000004308503],"category_scores_gemma":[0.001885892,0.0001366568,0.0001037886,0.001074181,0.00003239966,0.0009082651,0.000239527,0.0001402148,0.000006025408],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001113529,"about_ca_system_score_gemma":0.00007515073,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009855731,"about_ca_topic_score_gemma":0.000001764517,"domain_scores_codex":[0.9983866,0.0001058368,0.0003447199,0.000445017,0.0004218114,0.0002960356],"domain_scores_gemma":[0.997744,0.0009517731,0.0001682112,0.0008575689,0.0001292112,0.000149212],"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":[0.0004031078,0.00170945,0.001840406,0.00006792794,0.01026826,0.0001171658,0.00199158,0.5096317,0.0222707,0.1449854,0.006128761,0.3005855],"study_design_scores_gemma":[0.001828894,0.0004138645,0.0008053533,0.00009795847,0.001486117,0.00002831532,0.00005778418,0.955556,0.00979694,0.00009392615,0.02921285,0.000622028],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0006217256,0.00003439181,0.99626,0.001771854,0.0001296523,0.000231955,0.00004915814,0.0002982909,0.0006029761],"genre_scores_gemma":[0.8224759,0.000005500434,0.1772176,0.0001863853,0.00003987534,0.00005301846,0.000002954658,0.000008661155,0.00001011726],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8218542,"threshold_uncertainty_score":0.5572704,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0243301452670849,"score_gpt":0.2558899702717198,"score_spread":0.2315598250046349,"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."}}