A meta-analysis reveals that operational parameters influence levels of antibiotic resistance genes during anaerobic digestion of animal manures
Bibliographic record
Abstract
Anaerobic digestion (AD) has shown the potential to reduce the numbers and types of antibiotic-resistance genes (ARG) present in animal manures. However, the variability of the results has limited the ability to draw solid conclusions. To address this issue, we performed a series of meta-analyses to evaluate how AD of pig, cattle, and dairy manures affects ARG levels and how different parameters, such as temperature, pH, digestion times, and the addition of other substances (e.g., solids, antibiotics) influence ARG changes. Twenty studies with enough details on changes in ARG levels during the AD process were identified and used for the meta-analyses. The results suggested that AD could significantly reduce ARG levels regardless of the conditions of the process. Also, thermophilic AD was more effective than mesophilic AD at reducing ARGs, although this difference was only significant for pig manures. The results also suggested that long digestion times (>50 days) yielded better ARG reduction rates, and that the addition of solids from an external source (co-digestion) negatively affected the efficiency of ARG reduction. In general, the results suggested that ARG changes during AD could be linked to the abundance and activity of hydrolytic communities.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.018 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".