The effects of pig manure application on the spread of tetracycline resistance in bulk and cucumber rhizosphere soils: a greenhouse experiment
Bibliographic record
Abstract
It is important to understand the dynamics of tetracycline-resistant bacteria (TRB) and tetracycline resistance genes (TRGs) in bulk and rhizosphere soils for evaluating the spread of TRGs from pig manure to human. In this work, a greenhouse experiment was conducted to investigate the difference in abundance of TRB, tetracycline-resistant Escherichia coli (TRE), tetracycline-resistant Pseudomonas spp. (TRP), and TRGs between bulk and cucumber rhizosphere soils. The application of pig manure resulted in the long-term persistence of TRB, TRE, TRP, and TRGs in bulk soil and rhizosphere of cucumber for at least 65 days. Pig manure application dose was the major driving force in altering the abundances of TRB and TRE, whereas TRP was disturbed mainly by compartment (bulk soil or rhizosphere). Both TRE and the percentage of TRE in bulk and rhizosphere soils increased linearly with an increase in dose of pig manure. The exponential relationships between pig manure dose and TRP along with TRP percentage were also noted. There were significant differences in the relative abundances of TRGs between bulk and cucumber rhizosphere soils, suggesting the use of pig manure exerted a more lasting impact on the spread of TRGs in the rhizosphere than in the bulk soil.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".