{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000370318,0.0002350796,0.0001766976,0.00009883685,0.0003269197,0.000100621,0.00007270189,0.000152824,0.00004790616],"category_scores_gemma":[0.00001624207,0.0001712013,0.00007867281,0.0002100596,0.00003197358,0.0001143163,0.000009110177,0.0004989771,0.00006354453],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002147307,"about_ca_system_score_gemma":0.000007968995,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002862133,"about_ca_topic_score_gemma":0.000004907623,"domain_scores_codex":[0.9988979,0.0001528591,0.0002117163,0.0003147979,0.0002280231,0.0001946905],"domain_scores_gemma":[0.9996629,0.000108564,0.00003660307,0.0001257627,0.000005702657,0.0000604569],"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":[0.0001962447,0.00004459049,0.00007079962,0.001104173,0.00006171761,0.000006562264,0.0001709886,0.9286594,0.005548983,0.00001228982,0.0000159161,0.06410833],"study_design_scores_gemma":[0.0005376283,0.00008523823,0.0001354744,0.0003411617,0.00003459265,0.00001454728,0.0003850544,0.9814323,0.005786039,0.000004025913,0.01103627,0.0002077165],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007589814,0.001210789,0.9873382,0.0005694116,0.000478239,0.001208866,0.00003101468,0.001137058,0.0004365915],"genre_scores_gemma":[0.9983411,0.00004433045,0.0002464507,0.00005167251,0.0001083875,0.0009915039,0.00002005635,0.00005014591,0.0001463557],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9907513,"threshold_uncertainty_score":0.6981385,"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."}}