Safely Coupling Livestock and Crop Production Systems: How Rapidly Do Antibiotic Resistance Genes Dissipate in Soil following a Commercial Application of Swine or Dairy Manure?
Bibliographic record
Abstract
Animal manures recycled onto crop production land carry antibiotic-resistant bacteria. The present study evaluated the fate in soil of selected genes associated with antibiotic resistance or genetic mobility in field plots cropped to vegetables and managed according to normal farming practice. Referenced to unmanured soil, fertilization with swine or dairy manure increased the relative abundance of the gene targets sul1, erm(B), str(B), int1, and IncW repA. Following manure application in the spring of 2012, gene copy number decayed exponentially, reaching background levels by the fall of 2012. In contrast, gene copy number following manure application in the fall of 2012 or spring of 2013 increased significantly in the weeks following application and then declined. In both cases, the relative abundance of gene copy numbers had not returned to background levels by the fall of 2013. Overall, these results suggest that under conditions characteristic of agriculture in a humid continental climate, a 1-year period following a commercial application of raw manure is sufficient to ensure that an additional soil burden of antibiotic resistance genes approaches background. The relative abundance of several gene targets exceeded background during the growing season following a spring application or an application done the previous fall. Results from the present study reinforce the advisability of treating manure prior to use in crop production systems.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".