{"id":"W1978991828","doi":"10.1002/aic.12794","title":"Multiway discrete hidden Markov model‐based approach for dynamic batch process monitoring and fault classification","year":2011,"lang":"en","type":"article","venue":"AIChE Journal","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Fault detection and isolation; Computer science; Principal component analysis; Process (computing); Batch processing; Fault (geology); Pattern recognition (psychology); Algorithm; Artificial intelligence; Data mining","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":[0.0008422197,0.0005756148,0.0007825083,0.0008102762,0.0003308929,0.0006502387,0.001041112,0.0007382079,0.001352123],"category_scores_gemma":[0.001933433,0.0004420973,0.000856713,0.0004938034,0.0004258413,0.000772335,0.0005569046,0.001092145,0.0003168399],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009764689,"about_ca_system_score_gemma":0.0009802586,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009883896,"about_ca_topic_score_gemma":0.007925897,"domain_scores_codex":[0.9994796,0.000169358,0.00003203723,0.0001322123,0.0001247091,0.00006203027],"domain_scores_gemma":[0.9991935,0.0004932004,0.00009193498,0.00007107112,0.0001194933,0.00003082468],"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":[0.0001813236,0.00009016861,0.001946308,0.00006878598,0.00009735447,0.00008645123,0.00006716238,0.8550781,0.004315909,0.006078325,0.0007872207,0.1312029],"study_design_scores_gemma":[0.00000146906,0.000005034058,0.00009706654,9.133788e-7,0.000002500526,0.000003339605,0.000001204992,0.9988195,0.0002996059,0.0007154313,0.00005126978,0.000002545275],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01522501,0.0001361876,0.9835946,0.00008938902,0.00001884453,0.00001718557,0.00007202433,0.000473789,0.0003730239],"genre_scores_gemma":[0.7546684,0.00015222,0.2423905,0.00009045627,0.00004166688,0.0001403978,0.0003103113,0.00006947517,0.002136643],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009883896,"threshold_uncertainty_score":0.01965272,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02560534899649666,"score_gpt":0.2595610714805964,"score_spread":0.2339557224840998,"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."}}