{"id":"W2087617273","doi":"10.1021/ie034020w","title":"Model Predictive Monitoring for Batch Processes","year":2004,"lang":"en","type":"article","venue":"Industrial & Engineering Chemistry Research","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":84,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Missing data; Principal component analysis; Data mining; Process (computing); Batch processing; Trajectory; Multivariate statistics; Projection (relational algebra); Machine learning; Artificial intelligence; Algorithm","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001484845,0.0007815143,0.001064374,0.0004223291,0.0004314034,0.0009738884,0.001059367,0.0005785449,0.000850853],"category_scores_gemma":[0.0036562,0.0003855524,0.0005014928,0.0007631988,0.0006100101,0.0009613682,0.0006666344,0.001677822,0.0002394786],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007348963,"about_ca_system_score_gemma":0.0009173187,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009249074,"about_ca_topic_score_gemma":0.004944064,"domain_scores_codex":[0.9994481,0.000139219,0.00002173865,0.0001611046,0.0001832294,0.00004659661],"domain_scores_gemma":[0.9986672,0.0008006265,0.0001710297,0.0001309672,0.0002042479,0.00002581924],"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":[0.000181372,0.00004849716,0.001116172,0.0001085195,0.00003954002,0.00006014144,0.00008527011,0.8882788,0.003471951,0.01107438,0.001064324,0.09447095],"study_design_scores_gemma":[0.000002315738,0.0000134653,0.0001285569,0.000001862608,0.000002699598,0.000003405096,0.000002253184,0.9969645,0.0004790374,0.002205797,0.0001929104,0.000003141469],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01965726,0.00033996,0.9780658,0.0001186137,0.0000379233,0.00002850881,0.00006955394,0.0006308967,0.001051372],"genre_scores_gemma":[0.859882,0.0006358676,0.1355472,0.00006284632,0.00006565867,0.000149017,0.0002717382,0.00006927212,0.003316343],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009249074,"threshold_uncertainty_score":0.01839048,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09365285946362081,"score_gpt":0.3236941659306564,"score_spread":0.2300413064670356,"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."}}