{"id":"W3177807774","doi":"10.1111/1758-2229.12990","title":"Metagenomic and metatranscriptomic analysis reveals enrichment for <scp>xenobiotic‐degrading</scp> bacterial specialists and <scp>xenobiotic‐degrading</scp> genes in a Canadian Prairie <scp>two‐cell</scp> biobed system","year":2021,"lang":"en","type":"article","venue":"Environmental Microbiology Reports","topic":"Pesticide and Herbicide Environmental Studies","field":"Environmental Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada; Environment and Climate Change Canada; University of Regina","funders":"Research and Development; Agriculture and Agri-Food Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Xenobiotic; Biology; Metagenomics; Mesorhizobium; Bioremediation; Stenotrophomonas; Bacteria; Microbiology; Gene; Pseudomonas; Computational biology; Biochemistry; Genetics; Symbiosis","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.0001208036,0.0005017684,0.000348435,0.0005981646,0.0006780742,0.0007026427,0.0003953867,0.0003562266,0.0004298433],"category_scores_gemma":[0.0001838848,0.0001936252,0.0003533645,0.0008168171,0.0002826004,0.0002491992,0.0003605032,0.0005384093,0.0001305221],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002141539,"about_ca_system_score_gemma":0.003145486,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.4068494,"about_ca_topic_score_gemma":0.540136,"domain_scores_codex":[0.9996511,0.000009204714,0.00001287697,0.0001141881,0.000135494,0.00007720741],"domain_scores_gemma":[0.9998593,0.00001025533,0.00001995028,0.00000836716,0.00006250403,0.00003969124],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00009228958,0.00003231732,0.007176753,0.00003286725,0.00001682229,0.0000553326,0.0001255979,0.0001286129,0.9905252,0.0000238739,0.00003508534,0.001755292],"study_design_scores_gemma":[0.00002343254,0.0005641806,0.629261,0.00001695992,0.0001554182,0.0002750085,0.002088741,0.003960386,0.3597817,0.00006593094,0.003751267,0.00005591363],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9969119,0.000151646,0.0005866787,0.00004215477,0.000003861879,0.00004762451,0.001635369,0.00001647165,0.0006043416],"genre_scores_gemma":[0.9891576,0.0004616936,0.003621583,0.00007866314,0.000002691209,0.00005333082,0.003670332,0.00001189879,0.002942223],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5931506,"threshold_uncertainty_score":0.8089625,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007328922722999298,"score_gpt":0.2032989268252877,"score_spread":0.1959700041022884,"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."}}