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 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 | 0.001 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".