Investigation on microbial inactivation and urea decomposition in human urine during thermal storage
Bibliographic record
Abstract
The World Health Organization suggests storing human urine for at least 6 months at 20 °C prior to application as fertilizer to reduce the potential health risks from pathogenic organisms. Such a storage condition for human urine, however, not only requires a long period of time and large space but also ignores the risk of nitrogen losses. In this study, human urine underwent thermal treatment during storage to improve disinfection and to inhibit urea hydrolysis. Microbial indicators such as Escherichia coli and fecal coliforms and the concentration of ammonia/ammonium were investigated in urine samples that were stored at 60 °C and 70 °C. Both the inactivation of indicators and decomposition of urea improved under storage temperatures of 60 °C and 70 °C compared with storage under ambient temperature. Therefore, human urine is recommended to be stored at 70 °C for 7 days for hygienic and stabilization purposes. Under this storage condition, pH is maintained below 8.0 and ammonia/ammonium content is maintained at approximately 800 mg/L.
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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".