Metagenomic and Metatranscriptomic Analysis Reveals Enrichment for Xenobiotic-Degrading Bacterial Specialists and Xenobiotic-Degrading Genes in a Canadian Prairie Two-Cell Biobed System
Bibliographic record
Abstract
Abstract Biobeds are agriculture-based bioremediation tools used to safely contain and microbially degrade on-farm pesticide waste and rinsate, thereby reducing the negative environmental impacts associated with pesticide use. While these engineered ecosystems demonstrate efficient pesticide removal, their microbiome dynamics remain largely understudied both taxonomically and functionally. As such, further characterization of these parameters may aid in the optimization of biobed management and deployment. This study used metagenomic and metatranscriptomic techniques to characterize the microbial community in a two-cell Canadian biobed system before and after a field season of pesticide application. These culture-independent approaches identified an enrichment of xenobiotic-degrading bacteria, such as Afipia , Sphingopyxis and Pseudomonas , and enrichment and transcription of xenobiotic-degrading genes, such as peroxidases, oxygenases, and hydroxylases, among others; we were able to directly link the transcription of these genes to Pseudomonas , Oligotropha , Mesorhizobium , Rhodopseudomonas , and Stenotrophomonas taxa.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".