Sediment-Removal Efficiency of Vegetative Filter Strips
Bibliographic record
Abstract
Field experiments on vegetative filter strips (VFS) showed average sediment-removalefficiency varied from 50 to 98% as flowpath length increased from 2.44 to 19.52 m. Almost allof the easily removable aggregates (i.e. aggregates larger that 40 mm in diameter) can becaptured within the first five meters of the filter strip. However, the remaining small-sizeaggregates are very difficult to remove by filtering flow through grass media, as even relativelylow levels of turbulent energy in the water is sufficient to keep the finer sediments insuspension. The only effective mechanism for removal of small-size sediments is infiltration.Experiments with appreciable infiltration (low to moderate flow rates on the longer plot lengths),showed removal efficiencies of 90% or higher. The sediment-removal efficiency of the filter stripdoes not increase much by increasing the width of the filter strip beyond ten meters. Improvedefficiency of VFS can be achieved through the installation of a drainage system to increaseinfiltration.
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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 | 0.001 | 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".