{"id":"W4391006188","doi":"10.1002/cjce.25168","title":"Distributed temporal–spatial neighbourhood enhanced variational autoencoder for multiunit industrial plant‐wide process monitoring","year":2024,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Autoencoder; Computer science; Neighbourhood (mathematics); Benchmark (surveying); Process (computing); Data mining; Artificial intelligence; Pairwise comparison; Pattern recognition (psychology); Artificial neural network; Mathematics; Geography; Cartography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002299225,0.0001663536,0.0002153362,0.0001524143,0.0000663838,0.0001690716,0.0002211436,0.0001497187,0.00002561977],"category_scores_gemma":[0.0002802947,0.00013652,0.000118717,0.0002146591,0.00001605836,0.000147939,0.000004039299,0.0005127858,0.000003027239],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003350697,"about_ca_system_score_gemma":0.0003401367,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004237236,"about_ca_topic_score_gemma":0.000128776,"domain_scores_codex":[0.9989913,0.000009356252,0.0004029895,0.00009401872,0.0001898116,0.0003125836],"domain_scores_gemma":[0.9992589,0.0002145745,0.00004789866,0.00008352549,0.00009052685,0.0003045888],"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":[0.00004313924,0.000006206845,0.0001281718,0.0002105005,0.0003916065,0.00003793309,0.0007007105,0.9277886,0.06639574,0.000201399,0.0007824506,0.003313559],"study_design_scores_gemma":[0.0006709926,0.00002481357,0.00006223492,0.0003144456,0.00004893397,0.00007746425,0.00003201353,0.9246072,0.07038385,0.00007716169,0.003497088,0.0002037763],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4774582,0.001358925,0.5076924,0.001019166,0.0107574,0.0006594438,0.0004982603,0.0004212995,0.0001349362],"genre_scores_gemma":[0.9978157,9.196356e-7,0.0002063485,0.000007471312,0.001872707,0.00002749054,0.00001774687,0.00004105952,0.00001050143],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5203575,"threshold_uncertainty_score":0.5567123,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01219218814949078,"score_gpt":0.2104957053433433,"score_spread":0.1983035171938525,"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."}}