Impact of Fertilizing with Raw or Anaerobically Digested Sewage Sludge on the Abundance of Antibiotic-Resistant Coliforms, Antibiotic Resistance Genes, and Pathogenic Bacteria in Soil and on Vegetables at Harvest
Bibliographic record
Abstract
The consumption of crops fertilized with human waste represents a potential route of exposure to antibiotic-resistant fecal bacteria. The present study evaluated the abundance of bacteria and antibiotic resistance genes by using both culture-dependent and molecular methods. Various vegetables (lettuce, carrots, radish, and tomatoes) were sown into field plots fertilized inorganically or with class B biosolids or untreated municipal sewage sludge and harvested when of marketable quality. Analysis of viable pathogenic bacteria or antibiotic-resistant coliform bacteria by plate counts did not reveal significant treatment effects of fertilization with class B biosolids or untreated sewage sludge on the vegetables. Numerous targeted genes associated with antibiotic resistance and mobile genetic elements were detected by PCR in soil and on vegetables at harvest from plots that received no organic amendment. However, in the season of application, vegetables harvested from plots treated with either material carried gene targets not detected in the absence of amendment. Several gene targets evaluated by using quantitative PCR (qPCR) were considerably more abundant on vegetables harvested from sewage sludge-treated plots than on vegetables from control plots in the season of application, whereas vegetables harvested the following year revealed no treatment effect. Overall, the results of the present study suggest that producing vegetable crops in ground fertilized with human waste without appropriate delay or pretreatment will result in an additional burden of antibiotic resistance genes on harvested crops. Managing human exposure to antibiotic resistance genes carried in human waste must be undertaken through judicious agricultural practice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".