{"id":"W3126026012","doi":"10.1186/s12934-021-01509-2","title":"Engineering Escherichia coli for the utilization of ethylene glycol","year":2021,"lang":"en","type":"article","venue":"Microbial Cell Factories","topic":"Microbial Metabolic Engineering and Bioproduction","field":"Biochemistry, Genetics and Molecular Biology","cited_by":55,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Bioreactor; Petrochemical; Biochemical engineering; Metabolic engineering; Raw material; Pulp and paper industry; Ethylene glycol; Microbial consortium; Fermentation; Commodity chemicals; Assimilation (phonology); Biotechnology; Chemistry; Environmental science; Food science; Organic chemistry; Biology; Bacteria; Microorganism; Engineering","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":[0.00007228986,0.0001051176,0.0001088098,0.00001380241,0.00005636538,0.00001762188,0.0000869593,0.00009534982,0.00001536282],"category_scores_gemma":[0.0001170717,0.00008804636,0.00007691229,0.0001083058,0.00002805219,0.000002846982,0.00004029162,0.00005101772,0.000001139746],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000706934,"about_ca_system_score_gemma":0.00005511726,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001212056,"about_ca_topic_score_gemma":0.000009940125,"domain_scores_codex":[0.9994821,0.00001337759,0.0001464189,0.0001834484,0.0000422058,0.0001324595],"domain_scores_gemma":[0.9995565,0.00001023315,0.00004836119,0.0002101188,0.0001544772,0.00002027874],"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":[0.00002888861,0.00002506021,0.00003085834,0.0000845915,0.00002690041,1.258404e-7,0.00006939415,0.000951053,0.9968618,0.00004015623,0.001608692,0.0002725328],"study_design_scores_gemma":[0.0001634674,0.00002869581,0.0002200368,0.000005391924,0.000019648,0.000001635919,0.00003459781,0.0000323038,0.7401496,0.00000140523,0.2592675,0.00007577595],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9579167,0.00292352,0.03752123,0.00009236374,0.001202384,0.0002170489,0.00007163313,0.00001686379,0.00003829961],"genre_scores_gemma":[0.9956825,0.0004195844,0.002061019,0.00003404736,0.0004930533,0.00001309814,0.0001505522,0.00002016191,0.001126018],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2576588,"threshold_uncertainty_score":0.3590426,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01264731284187696,"score_gpt":0.2214546216697385,"score_spread":0.2088073088278615,"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."}}