{"id":"W3033020321","doi":"10.1016/b978-0-12-819025-8.00007-7","title":"Engineering bacterial aromatic dioxygenase genes to improve bioremediation","year":2020,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Microbial bioremediation and biosurfactants","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Alberta Ministry of Agriculture and Forestry","funders":"","keywords":"Dioxygenase; Bioremediation; Gene; Chemistry; Operon; Bacteria; Computational biology; Biochemistry; Biology; Genetics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0001242275,0.0006597108,0.0002257532,0.0003894687,0.00009604708,0.0006296497,0.0002881245,0.0004629755,0.001734658],"category_scores_gemma":[0.0001090377,0.000201232,0.0003270956,0.0005831125,0.0002113575,0.0005379507,0.0002845456,0.0006724499,0.001421022],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005720916,"about_ca_system_score_gemma":0.0001492124,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001077262,"about_ca_topic_score_gemma":0.001772686,"domain_scores_codex":[0.9999261,0.000005071102,0.0000049001,0.00001344646,0.0000373701,0.0000130609],"domain_scores_gemma":[0.9999779,0.000008297427,0.000003082696,0.000002193211,0.000005883532,0.000002676829],"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":[0.00003716757,0.0000914922,0.0001312302,0.0002044398,0.000006020757,0.00008288365,0.00004187392,0.0007627005,0.8806956,0.00191239,0.001101252,0.114933],"study_design_scores_gemma":[0.0000164305,0.0001614216,0.001257735,0.00006713613,0.00002764863,0.000254887,0.00005006292,0.002317706,0.8972626,0.001583259,0.0969868,0.00001425996],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5214806,0.1066519,0.1589738,0.0032041,0.001469641,0.0002093989,0.001816062,0.002194695,0.2039997],"genre_scores_gemma":[0.5505195,0.09413052,0.1155413,0.000959521,0.0001609695,0.0001361246,0.003249905,0.0005023225,0.2347999],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001734658,"threshold_uncertainty_score":0.005802989,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007903820329333134,"score_gpt":0.1864376655310811,"score_spread":0.1785338452017479,"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."}}