Impact of liquid hog manure applications on antibiotic resistance genes concentration in soil and drainage water in field crops
Bibliographic record
Abstract
Agricultural practices such as manure applications could contribute to the spread of antibiotic resistance genes (ARGs) within the environment. Our objective was to assess the impact of certain fertilization methods (mineral or manure) and tillage practices (reduced or conventional) on the presence of ARGs and bacteria in soil and drainage water under wheat and grain corn crops. Targeted ARGs tet(T), sul1, and blaCTX-M-1 in liquid hog manure, soil, and water samples were quantified by qPCR. Conventional PCR was used to detect mcr-1 and mcr-2. ARGs in control plots were detected despite the absence of manure, representing an environmental reservoir of resistant microorganisms. The manure application rate higher than 39 m3/ha increased tet(T) and sul1 gene concentrations in soil for more than 180 days. Tillage practices had no impact on ARG concentrations in soil and water samples. The blaCTX-M-1 gene was only detected in seven water samples in 2016, but no link was established with the treatments. The mcr-1 and mcr-2 genes were not detected in all tested samples. This study demonstrated that tet(T) and sul1 gene concentrations increased in soil after liquid hog manure application as well as in drainage water in the next weeks.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".