Weighted risk assessment of critical source areas for soil phosphorus losses through surface runoff mechanisms
Bibliographic record
Abstract
In intensive livestock areas, soils commonly contain elevated nutrients above the agronomic optimum which increases the risk of nutrient losses and contributing to poor ecological status waterbodies. Large within-field variability in soil nutrient content exists, and at-risk phosphorus (P) hotspots are rarely quantified due to sub-optimal soil sampling regimes. This study aims to address this issue by developing and evaluating an improved classification of P transfer risk at a sub-field scale through a weighted risk assessment model that combines gridded soil sampling data with modelled in-field surface runoff pathways. Within-field soil P variability was quantified at six field-scale sites in Northern Ireland using two different sampling techniques; traditional bulked field soil sampling (i.e. bulk analysis of W pattern sampling) and gridded sampling (at 35 m resolution) alongside interpolation. Results show that traditional bulked sampling failed to account for the sub-field scale spatial variability in soil P content. This may contribute to the poor chemical and ecological status of surface waters by frequently under-predicting soil nutrient content, and failing to identify potential contributing sources of soil P losses. In contrast, higher intensity gridded sampling and interpolation revealed wide in-field spatial variability in soil P content, facilitating the identification of contributing sources of P losses to poor water quality and aiding in the characterisation of risk for nutrient losses to waterways. Hydrological modelling of in-field runoff pathways indicated several P sources potentially contributing to runoff-based P losses. Our weighted risk assessment model was successful in identifying P hotspots and transfer potential to water courses, illustrating that a similar approach could be applied anywhere in the world where excess P poses a problem for water quality. Model validation took place using instream water quality sampling data, which showed that higher risk weighting model results correlated to poorer water quality conditions. This methodology could be a useful management tool to help countries meet their national water quality targets.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".