Mitigating water pollution in a Portuguese river basin under climate change through agricultural sustainable practices
Bibliographic record
Abstract
Abstract Sustainable agricultural practices have been extensively used to reduce water pollution; however, the effectiveness of these practices may be significantly affected by climate change. In this study, we assess the effectiveness of sustainable agricultural practices in reducing sediment and nutrient export to rivers under future climate conditions, using the Soil & Water Assessment Tool in a Portuguese river basin exposed to high levels of agricultural pollution. In our study, filter strips demonstrated a significant mitigation effect on sediment and nutrient increases under simulated climate change conditions, with maximum sediment export depletion reaching 65% and the highest nutrient export depletion observed at 35%. The implementation of multiple sustainable practices resulted in the highest depletions, with a notable 71% depletion in sediment export and a 35% depletion in nutrient export. Additionally, our research underscores the crucial role of filter strips and multiple sustainable agricultural practices in mitigating the projected rise in nutrient concentrations during summer, given the effect of climate change on river discharge. Further studies exploring sustainable agricultural practices across different climates and watersheds can improve our understanding of their effectiveness for adaptation to climate change.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".