{"id":"W4415438576","doi":"10.1021/acs.iecr.5c02567","title":"A Data-Driven Symbolic Regression Framework for Modeling and Multiobjective Optimization of a Microbial Fermentation System","year":2025,"lang":"en","type":"article","venue":"Industrial & Engineering Chemistry Research","topic":"Microbial Metabolic Engineering and Bioproduction","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Sorting; Symbolic regression; Bioprocess; Genetic algorithm; Support vector machine; Nonlinear system; Nonlinear regression; Multi-objective optimization","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":[0.00085204,0.0012402,0.0009419943,0.000635465,0.0003339371,0.001006449,0.0009581256,0.0009442411,0.001130375],"category_scores_gemma":[0.001365525,0.0004328924,0.001115093,0.0006767113,0.0005590586,0.0005226468,0.0007619105,0.001207337,0.0002622903],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008254841,"about_ca_system_score_gemma":0.001413304,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0112279,"about_ca_topic_score_gemma":0.006538029,"domain_scores_codex":[0.9996408,0.0001292577,0.00002332146,0.0000683076,0.0001051732,0.00003296412],"domain_scores_gemma":[0.9995643,0.0002487147,0.00005807625,0.00002227391,0.000093043,0.00001356044],"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":[0.000005319288,0.000005734844,0.00008479362,0.00002356204,0.000007652233,0.00001584872,0.00001031304,0.9944885,0.0006517717,0.001640595,0.00004405589,0.003021856],"study_design_scores_gemma":[8.150933e-7,0.000004406601,0.0000120628,0.000001671331,0.000001460796,0.000001438838,0.000001402893,0.9992994,0.0001437463,0.000424819,0.0001074217,0.000001357057],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01381353,0.0004209277,0.9828228,0.000145304,0.0000236585,0.00003266512,0.0001067634,0.0003356823,0.002298632],"genre_scores_gemma":[0.7704275,0.0009825844,0.2234058,0.00009216826,0.00004303252,0.0004682844,0.0004482463,0.0001452673,0.003987019],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0112279,"threshold_uncertainty_score":0.0223251,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06858205099088752,"score_gpt":0.3570621504152701,"score_spread":0.2884800994243826,"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."}}