Passive evaporation of source-separated urine from dry toilets: UES optimization and dry product accumulation over time
Bibliographic record
Abstract
A urine evaporation system (UES) was optimized and evaluated in a laboratory by adding 5 L of urine at the same time each day for 65 days. The UES consisted of a wooden box that is open at the front only with tracks for 22 vertically stacked cafeteria-type trays and a fan and chimney at the back. Urine flowed from tray to tray via gravity exiting each tray via a weir along the long side of the tray. A distinctive physical and chemical zonation in the solid urine product was observed from the upper to lower trays due to leaching of precipitated minerals in the upper trays and mineral accumulation in the lower trays. The redox conditions became increasingly oxidizing from the top to bottom trays due to contact with the atmosphere thus favouring more stable mineralized forms of nitrogen (ammonium and nitrate) and sulphur (sulphate) and disfavouring the less stable and volatile ammonia, nitrogen gas and hydrogen sulphide. The quality of the fertilizer product is higher in the upper trays with higher levels of nitrogen, phosphorus and potassium, whereas the lower trays have higher levels of sodium chloride. Nitrogen losses due to ammonia volatilization were approximately 35%.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".