Experimental investigation of runoff reduction and sediment removal by vegetated filter strips
Bibliographic record
Abstract
Abstract The impact of vegetated filter strips (VFS) on sediment removal from runoff has been studied extensively in recent years. Vegetation is believed to increase water infiltration and decrease water turbulence thus enhancing sediment deposition within filter media. In the study reported here, field experiments have been conducted to examine the efficiency of vegetated filter strips for sediment removal from cropland runoff. Twenty filters with varying length, slope and vegetated cover were used under simulated runoff conditions with an average sediment concentration of 2700 mg/L. The filters were 2, 5, 10 and 15 m long with a slope of 2·3 and 5% and three types of vegetation. Three other strips with bare soil were used as a control. The experimental results showed that the average sediment trapping efficiency of all filters was 84% and ranging from 68% in a 2‐m filter to as high as 98% in a 15‐m long filter compared with only 25% for the control. The length of filter has been found to be the predominant factor affecting sediment deposition in VFS up to 10 m. Increasing filter length to 15 m did not improve sediment trapping efficiency under the present experimental conditions. The rate of incoming flow and vegetation cover percentage has a secondary effect on sediment deposition in VFS. Copyright © 2004 John Wiley & Sons, Ltd.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".