Metadata and antimicrobial resistance gene count data from dusts collected on Canadian vehicle filters
Bibliographic record
Abstract
The role of bioaerosols in the dispersal of antimicrobial resistance genes (ARGs) and resistant microorganisms is poorly understood. In addition, bioaerosols are powerful composite samples representative of the surrounding environment and can be used as sentinels of many local habitats. Evidence suggests that using environmental DNA from dust collected on vehicle cabin air filters can define regional resistance profiles. Here, this method was used to investigate differences in resistance gene profiles, their underlying bacterial communities, and their links to anthropogenic and environmental variables across Canada. In total, 477 car filter samples were collected, with every province and territory being represented. DNA was extracted from filter dust. High‐throughput qPCR was used to detect and quantify a panel of 36 ARGs and 3 mobile genetic elements. Bacterial biomass was assessed using standard qPCR methods of the 16S rRNA gene, which was also used to assess bacterial biodiversity via metabarcoding. Results indicated that qepA dominates antimicrobial resistance profiles across Canada. However, after they were removed from the dataset, regional profiles were evident based on gene type and richness. Factors positively linked to total numbers of ARGs included human and livestock populations; whereas mean annual precipitation was negatively linked to resistance gene quantities. Measures of α‐diversity were generally greater in the western regions of Canada than in the east and the north. Community composition analyses showed similarities between the prairies and territories, which were separated from other regions. Finally, network analyses revealed a relatively stable group of core ARGs across regions, which were largely correlated with low‐abundance genera. Such findings suggest that rare taxa are key links in the diffusion of antimicrobial resistance in environmental contexts. Furthermore, this study highlights the potential application of vehicle air filters in building long‐term monitoring capacity of outdoor bioaerosols.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.010 | 0.014 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".