Needles in haystacks: monitoring the potential escape of bioaerosolised antibacterial resistance genes from wastewater treatment plants with air and phyllosphere sampling
Bibliographic record
Abstract
Wastewater treatment plants are well-known point sources of emissions of antibacterial resistance genes (ARGs) into the environment. Although most work to date has focused on ARG dispersal via effluent, aerial dispersal in bioaerosols is a poorly understood, but likely important vector for ARG dispersal. Recent evidence suggests that ARG profiles of the conifer needle phyllosphere could be used to measure bioaerosol dispersal from anthropogenic sources. Here, we assessed airborne dispersal of ARGs from wastewater treatment plants in Wales, UK and Quebec, Canada, using conifer needles as passive bioaerosol monitors. ARG profiles of wastewater were compared to those of conifer phyllosphere using high-throughput qPCR. ARG richness was significantly lower in conifer phyllosphere samples than wastewater samples, though no differences were observed across the dispersal gradients. Mean copy number of ARGs followed a similar trend. ARG profiles showed limited, but consistent patterns with increasing distance from wastewater treatment plants, but these did not align with those of wastewater samples. For example, proportional abundance of aminoglycosides decreased over the dispersal gradient in Wales, whereas mobile genetic elements showed the inverse relationship. In summary, while distinct ARG profiles exist along dispersal gradients, links to those of wastewater were not apparent.
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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) | 0.000 | 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".