{"id":"W4385587172","doi":"10.1016/b978-0-12-823869-1.00009-0","title":"Large-scale process models using deep learning","year":2023,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Deep learning; Artificial intelligence; Process (computing); Computer science; Scale (ratio); Identification (biology); Machine learning; Variety (cybernetics); Artificial neural network; Noise (video); Deep neural networks; Noisy data; 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":[0.0003224414,0.001119722,0.0008019254,0.0003744087,0.000179635,0.001049591,0.000907768,0.0009725936,0.006770505],"category_scores_gemma":[0.001062173,0.0008150996,0.0008582924,0.0007843886,0.0003497689,0.001418755,0.0009430319,0.002190553,0.003113404],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000472961,"about_ca_system_score_gemma":0.000529314,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005715942,"about_ca_topic_score_gemma":0.008044035,"domain_scores_codex":[0.999905,0.00001380517,0.000006223338,0.00003033121,0.00003621187,0.000008459268],"domain_scores_gemma":[0.9996148,0.0002183721,0.00002630914,0.00007466831,0.00005061058,0.00001512454],"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":[0.00002613635,0.00002575899,0.0002445408,0.00010895,0.00006821563,0.00004199206,0.00001515149,0.7942792,0.002099456,0.01105132,0.008921241,0.183118],"study_design_scores_gemma":[0.000001444902,0.000003551957,0.00006079018,0.000005811672,0.00000435625,0.000008356876,0.00000116117,0.9889097,0.0003378575,0.008921588,0.001742445,0.000002973301],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003510545,0.001701797,0.9861914,0.0004347037,0.0001720117,0.00001200109,0.0004145156,0.002228615,0.00533443],"genre_scores_gemma":[0.4082334,0.009015072,0.5109363,0.0005229796,0.0007050771,0.0002079347,0.00491325,0.001343387,0.06412274],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006770505,"threshold_uncertainty_score":0.02264953,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01658578920606174,"score_gpt":0.2307782766715302,"score_spread":0.2141924874654685,"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."}}