Effect of anaerobic digestate fuel pellet production on <i>Enterobacteriaceae</i> and <i>Salmonella</i> persistence
Bibliographic record
Abstract
Abstract Production of digestate pellets for fuel has been identified as a promising circular economy approach to provide renewable energy and additional income to farms, while at the same time presenting the potential to divert raw digestate from nutrient‐saturated land and reduce the risk to water quality. Although previous research has investigated the feasibility of pellet production, there has been little focus on the bio‐safety aspects of the system. Little is currently known about the persistence of bacteria present in the digestate and the potential impacts on human health for those handling this product. The aim of the present research was to determine the effect that each step in the pellet production process has on bacteria numbers: anaerobic digestion, mechanical separation, solid drying, and pelletisation. Enterobacteriaceae enumeration by colony count method was used to quantify bacteria, and the presence of Salmonella at each stage was determined. The Enterobacteriaceae count reduced with each stage, and the final pelletisation step reduced bacteria numbers to below detectable levels (<10 colony forming units/g). Salmonella was only detected in the starting slurry and absent from digestate onwards. Storage of the pellets under winter and simulated summer conditions showed no reactivation of Enterobacteriaceae over time. The pelletisation process produces a digestate product with Enterobacteriaceae counts below the maximum threshold (PAS110 specification) for transport off the source farm, but care must still be taken when handling digestate pellets as complete sterilisation has not been confirmed.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".