{"id":"W2166016126","doi":"","title":"Real-Time Monitoring of Complex Industrial Processes with Particle Filters","year":2002,"lang":"en","type":"article","venue":"","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":59,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Particle filter; Auxiliary particle filter; Overhead (engineering); Particle (ecology); Computer science; Variance (accounting); Algorithm; Fault (geology); Filtering problem; Fault detection and isolation; Sampling (signal processing); Artificial intelligence; Filter (signal processing); Kalman filter; Computer vision; Ensemble Kalman filter","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":[0.00002851133,0.00006721523,0.0001148965,0.00002193424,0.0000195096,0.00001544012,0.00004859626,0.00003198876,0.0003472825],"category_scores_gemma":[0.00001266703,0.00005351277,0.00001438375,0.0001698556,0.00001320493,0.00007190956,0.000003584506,0.00004014103,0.000057722],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000154045,"about_ca_system_score_gemma":0.000003962115,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005239417,"about_ca_topic_score_gemma":0.000007511585,"domain_scores_codex":[0.9995741,0.000008271088,0.0001343774,0.00006394804,0.000101343,0.0001179628],"domain_scores_gemma":[0.999793,0.00003210846,0.00001840682,0.00008851563,0.00002574384,0.00004224632],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001341887,0.000144344,0.03514678,0.000495901,0.0003396509,0.00001463265,0.001776141,0.05781388,0.8712705,0.00007517215,0.01221552,0.02057328],"study_design_scores_gemma":[0.004397998,0.0004513297,0.00369212,0.0002366707,0.00004867676,0.00002668893,0.001270348,0.5446796,0.4368465,0.000005455864,0.00777518,0.0005693763],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9805072,0.00002992172,0.0001411618,0.00002640251,0.0001121235,0.0001114902,0.000002065085,0.0003387462,0.01873088],"genre_scores_gemma":[0.9987676,0.00001379267,0.000142567,0.000001788441,0.0001087592,0.00001336716,3.450967e-7,0.00001238172,0.000939418],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4868658,"threshold_uncertainty_score":0.38025,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03991290758258402,"score_gpt":0.2193980812989852,"score_spread":0.1794851737164012,"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."}}