Occurrence of antimicrobials in animal manure‐amended soils around the breeding farms: The case of the Saigon River (southern Vietnam)
Bibliographic record
Abstract
Abstract The excessive use of antimicrobials in animal rearing and the associated environmental hazards have become a pressing issue. Animal agriculture is often viewed as a significant contributor to environmental degradation due to the residues of antimicrobials. It is a common practice to use livestock waste as a soil enhancer in farming. Despite some research into antimicrobials, there is room for more comprehensive data regarding these pollutants in animal farming environments. A handful of earlier studies have identified antimicrobials in animal waste. This research undertook the task of examining and evaluating soils amended with animal waste (from chickens, cows, and pigs) for the presence of seven specific antimicrobials. The antimicrobials under scrutiny included trimethoprim (TRI), ormethoprim (ORM), ofloxacin (OFL), norfloxacin (NOR), tetracycline (TET), chlortetracycline (CTE), and tylosin (TLS). Soil samples were collected from areas surrounding breeding farms located upstream of the Sai Gon River. These samples were then subjected to laboratory analysis, which involved solid‐phase extraction using ultrasonic waves and the application of high‐performance liquid chromatography‐tandem mass spectrometry (LCMS/MS) to identify the antimicrobials. TRI, which had the highest average concentration (2.603–91.304 μg/kg), and OFL, with the second highest average concentration (1.815–15.832 μg/kg), were detected in all soil samples amended with manure. CTE, with the third highest average concentration, was found in soils amended with cow and pig waste (1.625–15.486 μg/kg). ORM and TE, with lower average concentrations (0.595–1.318 μ and 11.537–13.569 μg/kg, respectively), were only detected in soils amended with chicken waste, while NOR was only found in soils amended with cow waste. These findings indicate that the use of antimicrobials in animal farming can negatively impact the soil ecosystem. Consequently, these results can contribute to the creation of guidelines for monitoring antimicrobial residues in agricultural ecosystems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".