Revealing the potential nutrient, ecotoxin and pathogen risks to freshwaters from livestock excreta
Bibliographic record
Abstract
Livestock excreta is a major pollutant in UK freshwaters, contributing significantly to eutrophication, pharmaceutical loading, pathogen transport, and ecotoxicological risks to aquatic biota. Environmental impact is likely to depend on on-farm livestock management and farming methods, including the management of direct excreta inputs (dung, urine, slurry amendments, manure spreading to land), and connectivity of land to surface waters. Allowing livestock direct access to watercourses can also influence the rate of urination and defecation, with cattle (dairy and beef) showing a 5–10% increase in frequency compared to those voiding excreta on land. This heightened deposition in streams is exacerbated during summer low flows where streams have less dilution capacity. Meanwhile slurry amendments to fields before rainfall events can generate transport of slurry and its constituent contaminants from land to watercourses which is widely reported. Here, we report the results of a major NERC-funded research programme QUANTUM in which we have characterised the contaminant profiles of livestock excreta (including solid manure, slurry, urine and dung from dairy cattle, beef cattle and sheep farming systems) to determine their nutrient, chemical contaminant and pathogen composition, to clarify the risks these materials may pose to biota in UK freshwaters.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".