{"id":"W2033995807","doi":"10.1109/acc.2010.5530678","title":"Designing experiments from noisy metabolomics data to refine constraint-based models","year":2010,"lang":"en","type":"article","venue":"","topic":"Microbial Metabolic Engineering and Bioproduction","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Metabolomics; Computer science; Constraint (computer-aided design); Data mining; Data set; Disjoint sets; Sampling (signal processing); Set (abstract data type); Data modeling; Sample (material); Variance (accounting); Algorithm; Bioinformatics; Artificial intelligence; Mathematics; Biology","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":[0.0001887676,0.0001396361,0.000128112,0.00003456022,0.00004080679,0.00002729166,0.0003365433,0.0001206257,0.0000580295],"category_scores_gemma":[0.00008852233,0.0001265013,0.00003009359,0.00006235265,0.00003283734,0.000006064889,0.0001449602,0.0001032642,0.00001640783],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000002962523,"about_ca_system_score_gemma":0.00004626918,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001349854,"about_ca_topic_score_gemma":0.00005017019,"domain_scores_codex":[0.9990842,0.00001661341,0.0001592452,0.0004870323,0.00007302899,0.0001799016],"domain_scores_gemma":[0.9989237,0.000003134674,0.00003131558,0.0008932641,0.00004937022,0.00009918252],"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":[0.00002843458,0.00002588704,0.00001393453,0.000001808934,0.00002599199,3.91884e-7,0.00001046864,0.0008854559,0.9946991,0.00007767047,0.00216119,0.002069663],"study_design_scores_gemma":[0.0002776892,0.00003011656,0.0000313821,0.000003303549,0.0000163647,0.000002646856,0.00001838286,0.001585021,0.9565806,0.00001714244,0.04126658,0.0001707874],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7403438,0.0001730314,0.2579601,0.0002123226,0.0006485289,0.0001389484,0.0001497008,0.00003991049,0.000333628],"genre_scores_gemma":[0.7610281,0.00001080864,0.2371928,0.0003666586,0.0004594005,0.000007485351,0.0006811888,0.00001792268,0.000235562],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03910539,"threshold_uncertainty_score":0.5158573,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04172473987053606,"score_gpt":0.2753942314511763,"score_spread":0.2336694915806403,"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."}}