Slowing the flow in pickering: quantifying the effect of catchment Woodland planting on flooding using the soil conservation service curve number method
Bibliographic record
Abstract
The Soil Conservation Service (SCS) Runoff Curve Number method has been successfully applied to the Pickering Beck catchment at Pickering in North Yorkshire to assess the impact of land use change on flood flows.While limited-scale woodland creation (3% of the catchment) was predicted to have a small effect on the range of peak flows studied (<1% to 4% reduction), in line with previous model applications in the catchment, the conversion of the 25% cover of existing woodland to improved grassland produced a large increase in peak flow, up to 41% for a 1 in 100-year event.These numbers need to be treated with particular caution since the SCS method remains to be validated for UK conditions, however, they support growing evidence that woodland creation and management could have a significant role to play in flood risk management.The SCS method provides a potentially powerful tool for evaluating the impact of land-use change and management on flood runoff, as well as for identifying areas where such measures could be most effective.
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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 | 0.000 | 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".