Effectiveness of soil in vegetated buffers to retain nutrients and sediment transported by concentrated runoff through deep gullies
Bibliographic record
Abstract
Little research has evaluated naturally vegetated buffers to retain pollutants in soil from concentrated runoff through deep (2–14 m) gullies. Soil enrichment in the flow path of 11 naturally vegetated gullies in southern Alberta, Canada, was used as a long-term signature of filtering during concentrated flow. Soil was sampled at three depth intervals (0–2.5, 2.5–5, and 5–10 cm) along two 50-m transects inside and outside the flow path of the vegetated gullies in each of 3 yr (2011–2013). The influence of soil type, flow path (inside vs. outside), distance into vegetated flow path, depth, and their interactions on enrichment of nutrients (NH4–N, NO3–N, soil test P (STP), total P) and particle size fractions (clay, silt, and sand) was determined. Significantly (P ≤ 0.05) greater enrichment of nutrients and specific particle size fractions inside than outside the flow path of the vegetated gully suggested that greater deposition occurred inside the concentrated flow path. In contrast, there was little evidence for enrichment of nutrients and sediment at the front or inlet of the buffer (except STP), or for infiltration of more soluble nutrients into the subsoil. Soil enrichment in buffers may reveal long-term filtering processes that may not be shown with short-term runoff experiments.
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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 | 0.001 |
| 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".