Investigations into the effect of different land use on field-saturated hydraulic conductivity in the Eddleston Water catchment
Bibliographic record
Abstract
Land use and management have an impact on the infiltration capacity of soil. It is thought that by changing the way we use and manage land we can increase infiltration into the soil, slow the flow of water through the catchment and reduce flood peaks. However, there are few observational data that directly measure changes in soil permeability in different land uses. This report describes field investigations into the role of land cover on soil permeability in part of the Eddleston catchment in the Scottish Borders as a pilot for a larger study in the future. \nInvestigations were carried out at Wester Deans Farm on a range of land use types: coniferous woodland, improved grassland, a ten year old broadleaved transverse strip and rough grazing grassland. Experiments to measure the hydraulic conductivity (Kfs) of soils underlying these land uses were conducted using a constant head well permeameter (Guelph permeameter). In total there were 129 infiltration tests conducted for this study; 41 in coniferous woodland, 33 in improved grassland, 24 in a broadleaved transverse strip and 31 in rough grazing grassland. \nResults indicate that median Kfs rates were highest in soils under rough grazing, and medians statistically similar to the coniferous woodland and 10 year old transverse strip woodlands. Highest individual results, and overall range, were obtained under woodlands where root systems are able to create pathways for water flow. The lowest Kfs rates were under improved grasslands where dense animal grazing is known to increase compaction of the surface. Statistical analysis showed Kfs under improved grasslands to be statistically lower than the three other land uses tested. This study illustrates the role that areas of rough grazing may play in increasing soil infiltration and storage, and may have a similar impact to tree planting. Further study is planned on extending the surveys, and using these data to help plan soil restoration strategies.
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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 | 0.001 | 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".