{"id":"W4390562681","doi":"10.1016/j.psep.2023.12.071","title":"A multi-feature-based fault diagnosis method based on the weighted timeliness broad learning system","year":2024,"lang":"en","type":"article","venue":"Process Safety and Environmental Protection","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Fault (geology); Benchmark (surveying); Process (computing); Computer science; Feature (linguistics); ALARM; Data mining; Feature extraction; Artificial intelligence; Task (project management); Pattern recognition (psychology); Engineering","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":[0.0006186049,0.0006850417,0.001038197,0.001186909,0.0004871046,0.0005346075,0.0009653567,0.0007512255,0.002038579],"category_scores_gemma":[0.001294199,0.0002527213,0.000485206,0.0007192197,0.00024459,0.001123024,0.0006307074,0.0006949731,0.0005495821],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004649384,"about_ca_system_score_gemma":0.0007763415,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004415688,"about_ca_topic_score_gemma":0.004026586,"domain_scores_codex":[0.9995714,0.0000457589,0.00003251724,0.0001462482,0.0001585754,0.00004550458],"domain_scores_gemma":[0.9994288,0.0001339563,0.00007245532,0.00004327858,0.00028468,0.00003692118],"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":[0.0003847098,0.000142082,0.001708011,0.000124005,0.00009920727,0.00009146256,0.0000512632,0.08507618,0.03510362,0.002051464,0.002123059,0.873045],"study_design_scores_gemma":[0.00001862791,0.0001080685,0.000875811,0.000005602216,0.00002836795,0.0000663916,0.000006468615,0.9922528,0.005214716,0.00065175,0.0007575845,0.00001384969],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01715241,0.0002846163,0.9808821,0.0000576385,0.00006196288,0.00003915246,0.00003913168,0.0009151557,0.0005678428],"genre_scores_gemma":[0.6068169,0.0003049187,0.3878041,0.0001697865,0.0001204117,0.0001567982,0.0002800337,0.0000998492,0.004247207],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004415688,"threshold_uncertainty_score":0.008780003,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007413526653305676,"score_gpt":0.2112930876880286,"score_spread":0.2038795610347229,"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."}}