Persistence of antibiotic resistance and plasmid-associated genes in soil following application of sewage sludge and abundance on vegetables at harvest
Bibliographic record
Abstract
Sewage sludge recovered from wastewater treatment plants contains antibiotic residues and is rich in antibiotic resistance genes, selected for and enriched in the digestive tracts of human using antibiotics. The use of sewage sludge as a crop fertilizer constitutes a potential route of human exposure to antibiotic resistance genes through consumption of contaminated crops. Several gene targets associated with antibiotic resistance (catA1, catB3, ereA, ereB, erm(B), str(A), str(B), qnrD, sul1, and mphA), mobile genetic elements (int1, mobA, IncW repA, IncP1 groups -α, -β, -δ, -γ, -ε), and bacterial 16S rRNA (rrnS) were quantified by qPCR from soil and vegetable samples obtained from unamended and sludge-amended plots at an experimental field in London, Ontario. The qPCR data reveals an increase in abundance of gene targets in the soil and vegetables samples, indicating that there is potential for additional crop exposure to antibiotic resistance genes carried within sewage sludge following field application. It is therefore advisable to allow an appropriate delay period before harvesting of vegetables for human consumption.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 |
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".