Metagenomic and metatranscriptomic analyses reveal that biobed systems can enrich for antibiotic resistance and genetic mobility genes
Bibliographic record
Abstract
Antibiotic resistance gene pollution in the environment has been identified as a potential contributor to the global issue of antibiotic resistance prevalence, creating a need to identify and characterize environmental reservoirs for antibiotic resistance genes. Because many polluted environments have been shown to contain elevated levels of antibiotic resistance genes, agriculturally based pesticide bioremediation systems called 'biobeds' could serve as environmental reservoirs for antibiotic resistance genes, although this has never been extensively explored. Metagenomic and metatranscriptomic analyses of an on-farm biobed system sampled before and after a season of pesticide use demonstrated that in situ pesticide applications applied to biobeds can enrich for multidrug, sulphonamide, aminoglycoside and beta-lactam resistance genes. Additionally, this study demonstrated an enrichment for genes associated with gene mobilization, such as genes involved in horizontal gene transfer and plasmid mobility, as well as transposons and integrases.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".