Microbial antibiotic resistance genes across an anthropogenic gradient in a Canadian High Arctic watershed
Bibliographic record
Abstract
Antibiotic resistance is one of the biggest challenges to public health. While the discovery of antibiotics has decreased pathogen-caused mortality, the overuse of these drugs has resulted in the increased transfer and evolution of antibiotic resistance genes (ARGs) in bacteria. ARGs naturally occur in wild bacterial communities, but are also found in increased concentrations in environments contaminated by wastewater effluent. Although such ARGs are relatively well described in temperate environments, little is known about the distribution and dissemination of these genes in the Arctic. We characterized the ARGs in microbial communities from aerosols, lakes and microbial mats around a remote Arctic hamlet using metagenomic approaches. Specific objectives were to (i) compare ARGs across habitats, (ii) to characterize ARG populations along a continuum of anthropogenically influenced environments, and (iii) to identify ARGs of viral origin. We identified ARGs in all habitats throughout the watershed, and found that microbial mats in the most impacted area had the highest diversity of ARGs relative to uncontaminated sites, which may be a remnant signal of wastewater effluent inputs in the area during the 20th century. Although we identified ARGs predominantly in bacterial genomes, our data suggests that mimiviruses may also harbor ARGs.
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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 | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".