Phosphorus Removal in Vegetated Filter Strips
Bibliographic record
Abstract
Vegetated filter strips (VFS) are used recently for removal, at or near the source, of sediment and sediment-bound chemicals from cropland runoff. Vegetation within the flowpath increases water infiltration and decreases water turbulence, thus enhancing pollutant removal by sedimentation within filter media and infiltration through the filter surface. Field experiments have been conducted to examine the efficiency of vegetated filter strips for phosphorus removal from cropland runoff with 20 filters with varying length (2 to 15 m), slope (2.3 and 5%), and vegetated cover, including bare-soil plots as control. Artificial runoff used in this study had an average phosphorus concentration of 2.37 mg L(-1) and a sediment concentration of 2700 mg L(-1). The average phosphorus trapping efficiency of all vegetated filters was 61% and ranged from 31% in a 2-m filter to 89% in a 15-m filter. Filter length has been found to be the predominant factor affecting P trapping in VFS. The rate of inflow, type of vegetation, and density of vegetation coverage had secondary influences on P removal. Short filters (2 and 5 m), which are somewhat effective in sediment removal, are much less effective in P removal. Increasing the filter length beyond 15 m is ineffective in enhancing sediment removal but is expected to further enhance P removal. Sediment deposition, infiltration, and plant adsorption are the primary mechanisms for phosphorus trapping in VFS.
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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) | 0.000 | 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".