Sources of Antibiotic Resistance Genes in a Rural River System
Bibliographic record
Abstract
The increasing prevalence of antibiotic resistance genes (ARGs) in the environment is problematic due to the risk of horizontal gene transfer and development of antibiotic resistant pathogenic bacteria. Using a suite of monitoring tools, this study aimed to investigate the sources of ARGs in a rural river system in Nova Scotia, Canada. The monitoring program specifically focused on the relative contribution of ARGs from a single tertiary‐level wastewater treatment plant (WWTP) in comparison to contributions from the upgradient rural, sparsely developed, watershed. The overall gene concentration significantly (p < 0.05) increased downstream from the WWTP, suggesting that tertiary‐level treatment still contributes ARGs to the environment. As a general trend, ARG concentrations upstream were found to decrease as proximity to human‐impacted areas decreased; however, many ARGs remained above detection limits in headwater river samples, which suggested their ubiquitous presence in this watershed in the absence of obvious pollution sources. Significant correlations with ARGs were found for HF183 human fecal marker, Escherichia coli, and some antibiotics, suggesting that these markers may be useful for prediction and understanding of ARG levels and sources in rural rivers. Core Ideas Tertiary wastewater treatment contributed antibiotic resistance genes (ARGs) to the river. ARG levels decreased as proximity to anthropogenic influence decreased. ARGs were observed at detectable levels even in undeveloped headwaters. High flow conditions correlated to high ARG loading in the river. Positive correlations were found between ARGs and fecal indicators.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".