{"id":"W2474203492","doi":"10.1021/acs.iecr.6b03331","title":"Identification of Dynamic Metabolic Flux Balance Models Based on Parametric Sensitivity Analysis","year":2017,"lang":"en","type":"article","venue":"Industrial & Engineering Chemistry Research","topic":"Microbial Metabolic Engineering and Bioproduction","field":"Biochemistry, Genetics and Molecular Biology","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Consejo Nacional de Ciencia y Tecnología, Guatemala; Consejo Nacional de Ciencia y Tecnología","keywords":"Sensitivity (control systems); Parametric statistics; Flux balance analysis; Identification (biology); Flux (metallurgy); Parametric model; Maximization; Biological system; Computer science; Mathematics; Mathematical optimization; Statistics; Chemistry; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001673833,0.0001825977,0.0003051323,0.0002409015,0.0001549309,0.00008869903,0.0003748293,0.0003225965,0.000009989522],"category_scores_gemma":[0.001961438,0.000192472,0.0001732366,0.0006353462,0.0001043165,0.0000121223,0.00009959152,0.0004323446,0.000003399229],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003483375,"about_ca_system_score_gemma":0.00009252159,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006020948,"about_ca_topic_score_gemma":0.000001644507,"domain_scores_codex":[0.9983494,0.00006447083,0.0003157291,0.0005114037,0.0004061642,0.0003528385],"domain_scores_gemma":[0.9981719,0.00002719657,0.000153162,0.001278749,0.0002664203,0.0001026175],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00004201769,0.00004628863,0.0001269606,0.0000257056,0.0001396089,0.000001399207,0.000002517122,0.2695751,0.7291667,0.000006536767,0.00006158437,0.0008055179],"study_design_scores_gemma":[0.000300433,0.00002136909,0.001268269,0.00001622297,0.00007151999,0.000001715925,0.000002958892,0.2692063,0.7285265,0.00000291,0.0004527763,0.0001290618],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9958158,0.0001943197,0.003296444,0.00009670814,0.0002048876,0.0001743102,0.00006564066,0.0000256507,0.0001261964],"genre_scores_gemma":[0.9987144,0.0000522155,0.00005908238,0.000002043853,0.0004624937,0.00002097035,0.0001373466,0.00002461864,0.0005268196],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003237362,"threshold_uncertainty_score":0.7848779,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04498826109252186,"score_gpt":0.3245319828999233,"score_spread":0.2795437218074014,"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."}}