{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001258277,0.001170207,0.001900617,0.0004587835,0.0007979028,0.0001950736,0.0004003274,0.0005592674,0.0002604235],"category_scores_gemma":[0.0002256881,0.001197042,0.0005218558,0.0006312047,0.001308908,0.0003527124,0.0006512919,0.0004596749,0.0001206303],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002408548,"about_ca_system_score_gemma":0.00009227001,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.01275103,"about_ca_topic_score_gemma":0.04475505,"domain_scores_codex":[0.9928256,0.0004687624,0.001743303,0.002551193,0.0003079243,0.002103167],"domain_scores_gemma":[0.9966501,0.0006989434,0.0008569966,0.0008702409,0.000009980982,0.0009137273],"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":[0.000006487307,0.000203051,0.3487681,0.00007417684,0.0006957478,0.0007652134,0.000952293,0.000137653,0.6465478,0.0000183457,0.001533145,0.0002980589],"study_design_scores_gemma":[0.00295614,0.0004913465,0.6918598,0.00009579588,0.003759734,0.00284615,0.004611387,0.0002204775,0.2050941,0.0002048675,0.0873002,0.000559972],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9861469,0.008794161,0.0001086596,0.00009418929,0.0008241552,0.001648187,0.0007639795,0.00007261385,0.001547177],"genre_scores_gemma":[0.9907667,0.0009953978,0.003403694,0.000384773,0.0002245937,0.0001814176,0.001042979,0.0001259871,0.002874437],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4414537,"threshold_uncertainty_score":0.9990479,"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."}}