{"id":"W1966446042","doi":"10.1002/aic.10147","title":"Multivariate monitoring of batch processes using batch‐to‐batch information","year":2004,"lang":"en","type":"article","venue":"AIChE Journal","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":53,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"McMaster University; Consejo Nacional de Ciencia y Tecnología; Minnesota Pollution Control Agency","keywords":"Computer science; Principal component analysis; Batch processing; Data mining; Multiprotocol Label Switching; Partial least squares regression; Process engineering; Multivariate statistics; Artificial intelligence; Engineering; Machine learning","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0006634548,0.0004805892,0.0004821442,0.0008116343,0.0002421862,0.0005752737,0.0004046797,0.000293264,0.0003937657],"category_scores_gemma":[0.001299653,0.0002103545,0.0002641122,0.0008523734,0.0003035285,0.0005344221,0.000328111,0.0005312238,0.0001277926],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004632167,"about_ca_system_score_gemma":0.0003311804,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001606671,"about_ca_topic_score_gemma":0.001171918,"domain_scores_codex":[0.9995815,0.00008893886,0.0000189204,0.0001023104,0.0001866023,0.00002164859],"domain_scores_gemma":[0.9994265,0.0002307535,0.0001279125,0.00006105526,0.0001310621,0.00002269897],"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":[0.001485219,0.0003971082,0.01919048,0.0003322165,0.0001758965,0.0001703804,0.0001878839,0.2442614,0.4078203,0.003251652,0.001561041,0.3211666],"study_design_scores_gemma":[0.00001855607,0.0001827613,0.008711998,0.000005257052,0.00002874961,0.00004162779,0.00001342355,0.9196271,0.06914637,0.001625901,0.0005673234,0.00003090796],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3817287,0.000523891,0.6145031,0.0001615876,0.00004800285,0.00007226611,0.0004550785,0.001340701,0.001166607],"genre_scores_gemma":[0.9382729,0.0002115477,0.06065358,0.00002088416,0.00002657329,0.00004374635,0.0002437492,0.00003679297,0.0004902077],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001606671,"threshold_uncertainty_score":0.003508747,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01350543818473816,"score_gpt":0.2485925196787969,"score_spread":0.2350870814940587,"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."}}