Validation of a vegetated filter strip model (VFSMOD)
Bibliographic record
Abstract
Abstract Vegetated filter strips (VFS) are designed to reduce sediment load and other pollutants into water bodies. However, adaptation of VFS in the field has been limited owing to lack of data about their efficiency and performance under natural field conditions. A number of models are available that simulate sediment transport and trapping in VFS, but there is a general lack of confidence in VFS models owing to limited validation studies and model limitations that prevent correct application of these models under field conditions. The objective of this study is to test and validate a process‐based model (VFSMOD) that simulates sediment trapping in VFS. This model links three submodels: modified Green–Ampt's infiltration, Quadratic overland flow submodel based on kinematic wave approximation and University of Kentucky sediment filtration model. A total of 20 VFS, 2, 5, 10 and 15 m long and with various vegetation covers, were tested under simulated sediment and runoff conditions. The results of these field experiments were used to validate the VFS model. The model requires 25 input parameters distributed over five input files. All input parameters were either measured or calculated using experimental data. The observed sediment trapping efficiencies varied from 65% in the 2‐m long VFS to 92% in the 10‐m long filters. No increase in sediment removal efficiency was observed at higher VFS length. Application of the VFS model to experimental data was satisfactory under the condition that actual flow widths are used in the model instead of the total filter width. Predicted and observed sediment trapping efficiencies and infiltration volume fitted very well, with a coefficient of determination ( R 2 ) of 0·9 and 0·95, respectively. Regression analyses revealed that the slope and intercept of the regression lines between predicted versus observed infiltration volume and trapping efficiency were not significantly different than the line of perfect agreement with a slope of 1·0 and intercept of 0·0. Copyright © 2001 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".