Assessing the risk of exogenously consumed pharmaceuticals in land-applied human urine
Bibliographic record
Abstract
Once separated, the use of urine as fertilizer is a particular attractive proposition and can significantly mitigate the release of nutrients and pharmaceutically active compounds (PhACs) to the environment. In the current study, a simple methodological framework is proposed for assessing risks that are posed by the land application of urine, which contains PhACs, in terms of 6 selected environmental and human-health endpoints. In total, 25 commonly used PhACs were conservatively assessed using the proposed methodology and results indicated that 14 of them may pose a risk with respect to either eco-toxicological or human-health endpoints. The receiving terrestrial environment was identified as the most susceptible of the eco-toxicological endpoints and hazard to human-health was most significant through food-chain transfer. The results highlight the need to consider the potential impacts associated with pharmaceuticals and the need to pre-treat urine to address the presence of problematic PhACs before it is applied on land.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".