{"id":"W4378895303","doi":"10.1016/j.jprocont.2023.103001","title":"Modeling and Bayesian inference for processes characterized by abrupt variations","year":2023,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Inference; Bayesian inference; Gaussian process; Bayesian probability; Latent variable; Cauchy distribution; System dynamics; Gaussian; Dynamic Bayesian network; Process (computing); Computer science; Statistical physics; Mathematics; Artificial intelligence; Statistics; Physics","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":[0.0002463883,0.0001059558,0.0002698823,0.0001391042,0.0000720895,0.0000884722,0.00009520396,0.00006066431,0.000005073654],"category_scores_gemma":[0.0002170954,0.00009123659,0.00004574097,0.0002180825,0.000007410571,0.0003098833,0.000002956808,0.0001136875,0.000002309073],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001826905,"about_ca_system_score_gemma":0.0000615997,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001681814,"about_ca_topic_score_gemma":0.0000038766,"domain_scores_codex":[0.9992044,0.00001326259,0.0003936569,0.00007936873,0.0001430475,0.0001662389],"domain_scores_gemma":[0.9993233,0.0001222739,0.0001084722,0.00004869175,0.0003124205,0.00008483748],"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":[0.001309426,0.0001862601,0.001781107,0.008615127,0.001622213,0.00002614855,0.005050123,0.6577879,0.2053116,0.0004369173,0.001734302,0.1161389],"study_design_scores_gemma":[0.00231685,0.0000694363,0.00004222766,0.00007680969,0.00004420765,0.00001659086,0.0001227432,0.9949766,0.0002688009,0.000454507,0.001501133,0.0001100535],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.060506,0.0008465783,0.9374702,0.0002896,0.0002809597,0.0003691648,0.00003820714,0.0001523076,0.00004698831],"genre_scores_gemma":[0.9993309,0.0001269938,0.00007879814,0.00004606641,0.0002285914,0.0001199932,0.000002514846,0.00002096326,0.00004519174],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9388249,"threshold_uncertainty_score":0.372052,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009966464224164867,"score_gpt":0.2554199060548382,"score_spread":0.2454534418306733,"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."}}