{"id":"W4221037329","doi":"10.1002/cjce.24401","title":"Slow‐varying batch process monitoring based on canonical variate analysis","year":2022,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Science Foundation of Tianjin City; National Natural Science Foundation of China","keywords":"Batch processing; Process (computing); Computer science; Random variate; Principal component analysis; Feature (linguistics); Kernel (algebra); Fault detection and isolation; Kernel density estimation; Biological system; Process engineering; Algorithm; Mathematics; Statistics; Artificial intelligence; Engineering; Random variable","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000729779,0.0009902489,0.0007829089,0.001671771,0.000349286,0.0007831664,0.0005921462,0.0004197213,0.0007865229],"category_scores_gemma":[0.002160508,0.0002819779,0.0007151146,0.001496627,0.0004301106,0.0007972392,0.0005056828,0.000663091,0.0002532636],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004305362,"about_ca_system_score_gemma":0.0007129945,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004107083,"about_ca_topic_score_gemma":0.002408996,"domain_scores_codex":[0.999218,0.0001804499,0.00003951777,0.0002322834,0.0002803177,0.000049481],"domain_scores_gemma":[0.9989102,0.0004079575,0.0001598243,0.00009169433,0.0003940369,0.00003627694],"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":[0.000664125,0.0002218642,0.0197141,0.0004146459,0.0002976443,0.0002415911,0.0002717435,0.2821082,0.1363372,0.007952885,0.002151109,0.549625],"study_design_scores_gemma":[0.000006688756,0.00005874707,0.003040596,0.000004991316,0.00002184273,0.000044283,0.00001363011,0.9820794,0.01325255,0.0008751791,0.0005764774,0.00002560505],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06100302,0.0002479194,0.9366715,0.00006434094,0.00004607264,0.00005873173,0.00009978605,0.001035472,0.0007730893],"genre_scores_gemma":[0.7967101,0.0003549529,0.2015448,0.00003126913,0.00003685262,0.0001004688,0.0002303831,0.0001018786,0.0008892838],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004107083,"threshold_uncertainty_score":0.008166373,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006224141506773884,"score_gpt":0.1933799585491259,"score_spread":0.187155817042352,"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."}}