Antibiotic resistome in groundwater and its association with mountain springs and river
Bibliographic record
Abstract
The distribution of antibiotic resistance genes (ARGs) in water sources potentially threatens drinking water safety. However, the sources of antibiotic resistome in groundwater are still under-investigated. Here, we evaluated the profiles of antibiotic resistome in peri-urban groundwater and its associated water sources (river and mountain spring) to characterize the antibiotic resistome from natural water sources on groundwater resistome. A total of 261 antibiotic resistome were detected in groundwater, mountain spring, and river samples. The relative abundances of ARGs and mobile genetic elements (MGEs) were significantly higher in the river samples than in spring water and groundwater samples. The resistome profiles were similar between groundwater and spring water but differed from the river samples. According to source tracking results, the groundwater resistome was likely to be derived from springs (28.0%-50.0%) and rivers (28.6%-48.6%), which share the same trend for the source tracking of bacterial communities. Bacterial α-diversity, bacterial β-diversity, and MGEs directly or indirectly affected the ARGs in groundwater samples. Although the abundance of groundwater resistome was not elevated by river and spring water, groundwater resistomes were diverse and may be derived from both river and spring water. We highlight the importance of groundwater resistome and its association with potential water sources, providing a better understanding and basis for the effective control of the ARG proliferation and dissemination in groundwater from exogenous water bodies in the future.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".