{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001993984,0.00053417,0.0001842822,0.0001644698,0.00008792607,0.0003632594,0.0003019851,0.0003431186,0.0002925564],"category_scores_gemma":[0.0001895881,0.0001336072,0.0003005292,0.0003378003,0.0001208767,0.0001753367,0.0002926186,0.0004279809,0.0002390535],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003591661,"about_ca_system_score_gemma":0.0003325808,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001388146,"about_ca_topic_score_gemma":0.001771492,"domain_scores_codex":[0.9998059,0.0000328492,0.00002115199,0.00003288847,0.00006887829,0.00003840145],"domain_scores_gemma":[0.9998918,0.00002483706,0.00004438312,0.0000101323,0.00001957989,0.000009327568],"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":[0.00005779946,0.0001483004,0.001103514,0.00006276843,0.000008836765,0.00008911022,0.00001639373,0.001171752,0.9944759,0.0001938666,0.000049952,0.002621753],"study_design_scores_gemma":[0.00001472662,0.0003653457,0.002303401,0.00001421208,0.00002716761,0.0002226332,0.00004807119,0.008537086,0.9852984,0.00008552145,0.003074159,0.00000930259],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9755042,0.0004591369,0.02169634,0.0002075209,0.0000311334,0.00008020099,0.0003338346,0.0001188504,0.001568908],"genre_scores_gemma":[0.964096,0.0007473363,0.03245143,0.00008661959,0.000004754369,0.00004825329,0.0006836258,0.00002936364,0.00185266],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001388146,"threshold_uncertainty_score":0.002760112,"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."}}