{"id":"W2016652826","doi":"10.1016/j.ymben.2013.05.004","title":"Metabolic engineering of Escherichia coli for limonene and perillyl alcohol production","year":2013,"lang":"en","type":"article","venue":"Metabolic Engineering","topic":"Microbial Metabolic Engineering and Bioproduction","field":"Biochemistry, Genetics and Molecular Biology","cited_by":417,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Lawrence Berkeley National Laboratory; Biological and Environmental Research; Directorate for Biological Sciences; Petroleum Technology Research Centre; Gyeongsang National University","keywords":"Metabolic engineering; Escherichia coli; Alcohol; Limonene; Production (economics); Chemistry; Biochemistry; Biotechnology; Food science; Biology; Enzyme; Economics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001736883,0.0004113729,0.0002166599,0.0003220992,0.0001931352,0.0004739128,0.0002816925,0.0002566646,0.0004471049],"category_scores_gemma":[0.0001978615,0.0001652874,0.0002835249,0.0004007522,0.0001469511,0.0001731636,0.0003071132,0.0004425014,0.0003276527],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004321109,"about_ca_system_score_gemma":0.0004813573,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002335191,"about_ca_topic_score_gemma":0.003266104,"domain_scores_codex":[0.9998171,0.00003439159,0.00001842289,0.00001914622,0.00006751332,0.00004348458],"domain_scores_gemma":[0.9999386,0.00001232039,0.00001075683,0.000006870742,0.00001747877,0.00001389384],"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":[0.0001232294,0.0000936007,0.0004160128,0.00004288529,0.000008374017,0.00009799367,0.00002043389,0.0006243562,0.9939942,0.0003822251,0.00004378316,0.00415289],"study_design_scores_gemma":[0.000006585804,0.0000755916,0.000700697,0.000002955734,0.00001241821,0.00008738034,0.00003067119,0.001126175,0.9961835,0.00004519975,0.001723388,0.000005453095],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.98348,0.0004501846,0.01289517,0.0002806128,0.00004553021,0.00006391147,0.0003957175,0.0001058484,0.002283156],"genre_scores_gemma":[0.9838638,0.0006426962,0.010859,0.0000629967,0.000004927784,0.00002857295,0.0005720147,0.00004178367,0.003924155],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002335191,"threshold_uncertainty_score":0.004643202,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006656740572559443,"score_gpt":0.2011067605871071,"score_spread":0.1944500200145477,"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."}}