GDVFS: A New Toolkit for Analysis and Design of Vegetative Filter Strips Using VFSMOD
Bibliographic record
Abstract
Abstract The Guelph design tool for vegetative filter strips, GDVFS, is a toolkit for the analysis and design of vegetative filter strips (VFSs). The upland hydrology model UH and the vegetative filter strip model VFSMOD (the two main components of GDVFS) were adopted from an existing interface (VFSMOD-W), and new nutrient and bacteria transport add-ons for UH and VFSMOD were incorporated into GDVFS. Other utilities and tools were also included in GDVFS to provide a capable toolkit for the analysis and design of VFSs. The published evaluation of computational procedures used in GDVFS indicates that these procedures perform very well in the estimation of VFS sediment and phosphorus removal efficiencies. According to these results, comparison of the predicted and observed values for sediment and phosphorus removal efficiencies indicates 10 and 20% error, respectively. This paper provides descriptions on the capabilities and methodology followed in the GDVFS toolkit.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".