{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002321754,0.0001227482,0.0001793971,0.000169408,0.00009611784,0.0000927368,0.000119911,0.00007623227,0.0000119321],"category_scores_gemma":[0.0001048123,0.0001137551,0.00005180051,0.0003399173,0.00000768064,0.0007133095,0.00001323509,0.0002391429,0.0000351868],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001715327,"about_ca_system_score_gemma":0.00007552979,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001286969,"about_ca_topic_score_gemma":0.000007419404,"domain_scores_codex":[0.9990227,0.00001618976,0.000446013,0.00005461465,0.0002499083,0.0002106369],"domain_scores_gemma":[0.9994493,0.00002216199,0.000114084,0.00009561138,0.0001997379,0.000119072],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00003598195,0.00002151607,0.002154183,0.0002575415,0.0001078917,0.000005048604,0.00469818,0.866768,0.115555,0.00001396196,0.00006485402,0.01031782],"study_design_scores_gemma":[0.01210438,0.0006204716,0.03781662,0.003779835,0.0002701137,0.001774398,0.01420306,0.1871972,0.7117434,0.0009797718,0.02748729,0.002023524],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.920875,0.0001794329,0.07688153,0.0000740053,0.001324231,0.0001259008,0.000002555028,0.0001028314,0.0004345688],"genre_scores_gemma":[0.9963906,0.00003479594,0.003132695,0.00001874247,0.0003861348,0.00000492632,4.336713e-7,0.00001706512,0.00001462431],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6795708,"threshold_uncertainty_score":0.4638799,"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."}}