{"id":"W4387364163","doi":"10.1016/j.jtbi.2023.111632","title":"A Markov constraint to uniquely identify elementary flux mode weights in unimolecular metabolic networks","year":2023,"lang":"en","type":"article","venue":"Journal of Theoretical Biology","topic":"Microbial Metabolic Engineering and Bioproduction","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ottawa Hospital; University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Markov chain; Flux (metallurgy); Metabolic network; Constraint (computer-aided design); Markov process; Steady state (chemistry); Flux balance analysis; Computer science; Applied mathematics; Mathematical optimization; State space; Mathematics; Chemistry; Statistics","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":[0.0008627548,0.0001555936,0.0003040997,0.0002448637,0.00002703306,0.00001292384,0.0002546128,0.0001968034,0.0000598899],"category_scores_gemma":[0.0002381093,0.0001233666,0.0001427684,0.000333061,0.0001942993,0.000003651522,0.0001177642,0.0002347771,0.00001176555],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001500283,"about_ca_system_score_gemma":0.0000446547,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008425663,"about_ca_topic_score_gemma":0.000005009088,"domain_scores_codex":[0.9986508,0.0002262924,0.0004665476,0.0002369154,0.00008046876,0.0003389941],"domain_scores_gemma":[0.9994366,0.00001662091,0.000100561,0.0002066514,0.0001007018,0.000138896],"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":[0.0001927479,0.00004281726,0.0004431242,0.000006688336,0.00009284705,0.00002473253,0.0000264433,0.001014699,0.9444522,0.04759896,0.000944028,0.005160681],"study_design_scores_gemma":[0.001966185,0.001572288,0.007525645,0.000118219,0.0001545279,0.0005851696,0.0001453507,0.001510492,0.8692688,0.0333578,0.08306517,0.000730307],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9863558,0.0006879267,0.01087786,0.001097689,0.0006913699,0.0001327854,0.00001376783,0.00001192438,0.0001308482],"genre_scores_gemma":[0.9961449,0.0005904849,0.00238228,0.000413211,0.0003353291,0.00000487207,0.00005849165,0.00001713404,0.00005328936],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08212114,"threshold_uncertainty_score":0.5030745,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005138882872365179,"score_gpt":0.2700483366621716,"score_spread":0.2649094537898064,"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."}}