Do animal husbandry operations contaminate groundwater sources with antimicrobial resistance: systematic review
Bibliographic record
Abstract
Antimicrobial resistance (AMR) is a critical global health concern. Animal husbandry operations are AMR hotspots due to heavy antibiotic use and dissemination of animal waste into the environment. In this systematic review, we examined the impact of swine, poultry, and cattle operations on AMR in groundwater. We searched PubMed, Web of Science, CAB Direct, and the North Carolina State University Agricultural and Environmental Science databases in June 2022. The search returned 2487 studies. Of the 23 eligible studies, 17 were conducted in high-income countries (primarily the USA, also Canada, Saudi Arabia, Cyprus), and 6 were conducted in a single upper-middle-income country (China). Studies investigated facilities for swine (13), poultry (4), cattle (3), and multiple types of animals (3). The sampling distance ranged from onsite to > 20 km from facilities; the majority of studies (19) sampled onsite. Most studies collected samples from monitoring wells; only 5 studies investigated private drinking water wells. AMR in groundwater was associated with animal husbandry operations in 74% (17/23) of all studies, 65% (11/17) of studies in high-income countries, and 100% (6/6) of studies in China. Contamination was mostly found in onsite wells, especially downgradient of waste lagoons, but also in offsite private wells up to 2-3 km away. Few studies reported weather data, but AMR contamination appeared to increase with rainy conditions. Future studies should sample private wells at varying distances from animal husbandry operations under different weather conditions and include low- and middle-income countries where food animal production is intensifying.
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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 | 0.005 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".