Impact of Manure Fertilization on the Abundance of Antibiotic-Resistant Bacteria and Frequency of Detection of Antibiotic Resistance Genes in Soil and on Vegetables at Harvest
Bibliographic record
Abstract
Consumption of vegetables represents a route of direct human exposure to bacteria found in soil. The present study evaluated the complement of bacteria resistant to various antibiotics on vegetables often eaten raw (tomato, cucumber, pepper, carrot, radish, lettuce) and how this might vary with growth in soil fertilized inorganically or with dairy or swine manure. Vegetables were sown into field plots immediately following fertilization and harvested when of marketable quality. Vegetable and soil samples were evaluated for viable antibiotic-resistant bacteria by plate count on Chromocult medium supplemented with antibiotics at clinical breakpoint concentrations. DNA was extracted from soil and vegetables and evaluated by PCR for the presence of 46 gene targets associated with plasmid incompatibility groups, integrons, or antibiotic resistance genes. Soil receiving manure was enriched in antibiotic-resistant bacteria and various antibiotic resistance determinants. There was no coherent corresponding increase in the abundance of antibiotic-resistant bacteria enumerated from any vegetable grown in manure-fertilized soil. Numerous antibiotic resistance determinants were detected in DNA extracted from vegetables grown in unmanured soil. A smaller number of determinants were additionally detected on vegetables grown only in manured and not in unmanured soil. Overall, consumption of raw vegetables represents a route of human exposure to antibiotic-resistant bacteria and resistance determinants naturally present in soil. However, the detection of some determinants on vegetables grown only in freshly manured soil reinforces the advisability of pretreating manure through composting or other stabilization processes or mandating offset times between manuring and harvesting vegetables for human consumption.
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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 | 0.001 |
| 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 | 0.000 |
| 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".