Enrichment of antibiotic resistance genes in soil receiving composts derived from swine manure, yard wastes, or food wastes, and evidence for multiyear persistence of swine <i>Clostridium</i> spp.
Bibliographic record
Abstract
The impact of amendment with swine manure compost (SMC), yard waste compost (YWC), or food waste compost (FWC) on the abundance of antibiotic resistance genes in soil was evaluated. Following a commercial-scale application of the composts in a field experiment, soils were sampled periodically for a decade, and archived air-dried. Soil DNA was extracted and gene targets quantified by qPCR. Compared with untreated control soil, all 3 amendment types increased the abundance of gene targets for up to 4 years postapplication. The abundance of several gene targets was much higher in soil amended with SMC than in soil receiving either YWC or FWC. The gene target ermB remained higher in the SMC treatment for a decade postapplication. Clostridia were significantly more abundant in the SMC-amended soil throughout the decade following application. Eight percent of Clostridium spp. isolates from the SMC treatment carried ermB. Overall, addition of organic amendments to soils has the potential to increase the abundance of antibiotic resistance genes. Amendments of fecal origin, such as SMC, will in addition entrain bacteria carrying antibiotic resistance genes. Environmentally recalcitrant clostridia, and the antibiotic resistance genes that they carry, will persist for many years under field conditions following the application of SMC.
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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 | 0.001 |
| 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".