Assessment of Pathogen Inactivation under Sub-composting Temperature in Lab-scale Compost Piles
Bibliographic record
Abstract
This study was conducted to assess the temperature profile and corresponding pathogen inactivation in lab-scale compost piles. The variation in temperature at different locations of piles and E. coli concentrations was evaluated. The experiment design included plastic containers of different height filled with organic feedstock. Cotton balls soaked with pathogens (E. coli and E. coli O157:H7) were placed inside the feedstocks at various depths. Subsequently, change in pathogen concentrations, feedstock characteristics, and temperature was monitored over time. Observations showed fluctuation in temperature of piles. The peak temperature (> 50 °C) was reached after two weeks of expertiment. The concentrations of E.coli and E. coli O157: H7 at different depths varied among piles during the 35 days of experiments. The reductions in E. coli concentrations ranged 1- 4 orders of magnitude. In certain piles, reduction in E. coli concentrations was followed by increased in E. coli levels indicating the possibility of perturbation of bacteria in the feedstock potentially at low temperature. We anticipate these preliminary results will provide additional insights on pathogen inactivation in compost system. The approach used here can be implemented at field-scale compost piles for assessing pathogen inactivation during compost process under field conditions.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".