Eco-friendly and sustainability assessment of technologies for nutrient recovery from human urine—a review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Nitrogen (N), phosphorus (P), and potassium (K) represent the primary components of commercial NPK fertilizer and are primarily derived from finite resources through complex and expensive processes. To ensure global food security, the development of sustainable and eco-friendly procedures for fertilizer production has gained attention. Humans generally excrete urine containing 11 g of N/L, 0.3 g of P/L of P and 1.5 g of K/L, which benefit plant growth. The recovery of these essential plant nutrients from human urine has become the focal point of increasing research endeavors. Despite the potential advantages of nutrient recovery from urine, this process is complicated, and the economic implications are substantial. Furthermore, human urine may harbor undesirable contaminants, such as pathogens, pharmaceutical residues, hormones, and elevated salt levels, which could be disseminated into the environment through agriculture. This study appraised various emerging technologies for nutrient recovery from human urine, considering their challenges, environmental impact, economic viability, and the overall sustainability of the processes. This review elucidated that most nutrient recovery technologies demonstrated elevated efficiency in nutrient recovery. Nevertheless, a recurrent oversight involves neglecting the potential transfer of contaminants and pathogens into environmental matrices. The complexity of these processes and their economic feasibility vary, with some proving intricate and economically unviable. Given that no singular technology fully mitigates these challenges, integrating two or more technologies appears imperative to address drawbacks and enhance overall system performance.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it