{"id":"W4389068196","doi":"10.1093/femsec/fiad153","title":"Microbial degradation of naphthenic acids using constructed wetland treatment systems: metabolic and genomic insights for improved bioremediation of process-affected water","year":2023,"lang":"en","type":"article","venue":"FEMS Microbiology Ecology","topic":"Petroleum Processing and Analysis","field":"Chemistry","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Geological Survey of Canada; University of Calgary; Natural Resources Canada; Université de Montréal; National Research Council Canada; Institut National de la Recherche Scientifique","funders":"Genome Alberta; Genome Canada","keywords":"Bioremediation; Environmental chemistry; Degradation (telecommunications); Microbial biodegradation; Chemistry; Biodegradation; Metabolic pathway; Microorganism; Biochemical engineering; Biochemistry; Biology; Contamination; Ecology; Organic chemistry; Bacteria; Metabolism; Computer science","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":[0.0001111566,0.0002060495,0.0006693918,0.0003144361,0.0001070585,0.0000102832,0.0001088359,0.000354188,0.00002063577],"category_scores_gemma":[0.00004566296,0.0001521029,0.00009703245,0.0001842211,0.0002596809,0.00004751042,0.00003637623,0.00006525502,0.000002437729],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006831115,"about_ca_system_score_gemma":0.0001284741,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007825407,"about_ca_topic_score_gemma":0.0001073561,"domain_scores_codex":[0.9987305,0.00006741152,0.0005158915,0.0003531714,0.00002008855,0.0003128963],"domain_scores_gemma":[0.9991387,0.0001036023,0.0003854743,0.0001526381,0.0001836874,0.0000359667],"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":[0.0001073015,0.00005913624,0.00725793,0.0002345731,0.0004338916,6.880945e-7,0.0002988695,0.00007025255,0.9910746,0.00000705755,0.00000691697,0.0004488233],"study_design_scores_gemma":[0.002462087,0.000146981,0.001179039,0.00002804758,0.0004400474,0.00005124499,0.0002565751,0.003831869,0.9909243,0.00002949491,0.0004775159,0.0001728058],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9987996,0.0004820727,0.00005372097,0.00002691353,0.0001703005,0.0001023904,0.0002988646,0.00005508403,0.00001100376],"genre_scores_gemma":[0.9976956,0.0001082664,0.0001973462,0.000006719571,0.00006729928,0.00003518068,0.001678566,0.00002106739,0.0001899112],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006078891,"threshold_uncertainty_score":0.6202577,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01214567189410888,"score_gpt":0.2383378446519931,"score_spread":0.2261921727578842,"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."}}