Potential of Biological Processes to Eliminate Antibiotics in Livestock Manure: An Overview
Bibliographic record
Abstract
Degrading antibiotics discharged in the livestock manure in a well-controlled bioprocess contributes to a more sustainable and environment-friendly livestock breeding. Although most antibiotics remain stable during manure storage, anaerobic digestion can degrade and remove them to various extents depending on the concentration and class of antibiotic, bioreactor operating conditions, type of feedstock and inoculum sources. Generally, antibiotics are degraded during composting > anaerobic digestion > manure storage > soil. Manure matrix variation influences extraction, quantification, and degradation of antibiotics, but it has not been well investigated. Fractioning of manure-laden antibiotics into liquid and solid phases and its effects on their anaerobic degradation and the contribution of abiotic (physical and chemical) versus biotic degradation mechanisms need to be quantified for various manures, antibiotics types, reactor designs and temperature of operations. More research is required to determine the kinetics of antibiotics' metabolites degradation during anaerobic digestion. Further investigations are required to assess the degradation of antibiotics during psychrophilic anaerobic digestion.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".