{"id":"W4407381304","doi":"10.1002/cjce.25623","title":"Neural ordinary differential equation‐based model predictive controller for regulating glucose concentration in a fed‐batch <scp>CHO</scp> cell bioreactor","year":2025,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Model predictive control; Ode; Benchmark (surveying); PID controller; Artificial neural network; Control theory (sociology); Controller (irrigation); Computer science; Chinese hamster ovary cell; Nonlinear system; Control engineering; Engineering; Control (management); Mathematics; Artificial intelligence; Temperature control; Biology; Cell culture; Applied mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001745366,0.0001844701,0.0003015238,0.0001873781,0.00005390629,0.00006340406,0.000196443,0.0001250232,0.000001349715],"category_scores_gemma":[0.0004117518,0.00016675,0.0001053132,0.0002372218,0.00002471195,0.0001990864,0.000006548079,0.0003189012,2.118521e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006409489,"about_ca_system_score_gemma":0.0002314218,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006601599,"about_ca_topic_score_gemma":0.00008085279,"domain_scores_codex":[0.9988688,0.00001872191,0.0005110619,0.0001114024,0.00013399,0.0003559906],"domain_scores_gemma":[0.9990579,0.0003821295,0.0001192052,0.000110256,0.0001516867,0.0001787834],"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":[0.00002307094,0.000004861701,0.00004854165,0.00004926937,0.00002871822,0.000001641555,0.0001367899,0.8672534,0.1319508,0.0001445649,0.0000705975,0.0002877876],"study_design_scores_gemma":[0.002060784,0.00002101229,0.00004513407,0.0001308655,0.00004169232,0.000002799615,0.0000176528,0.9543001,0.0431104,0.0001658365,0.00003735248,0.00006635247],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2851177,0.0003427166,0.7134829,0.0001031005,0.0003499451,0.0004758178,0.00002050108,0.0000361714,0.0000711324],"genre_scores_gemma":[0.9974518,0.000001311966,0.002235633,0.00002965657,0.0001572287,0.00005522127,0.0000136697,0.00003486135,0.00002055915],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7123341,"threshold_uncertainty_score":0.6799867,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00755655054581519,"score_gpt":0.1917742967072106,"score_spread":0.1842177461613954,"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."}}