Landscape controls on nitrate removal in stream riparian zones
Bibliographic record
Abstract
We examined how landscape hydrogeologic characteristics influence groundwater nitrate removal by eight stream riparian sites on glacial till and outwash landscapes in southern Ontario, Canada. During high water table periods in 2000–2002, mean NO3−‐N input concentrations from adjacent cropland to the riparian sites ranged from 0.15 to 44.7 mg L−1. Seven of the eight sites had a mean nitrate removal efficiency of >90%. This removal occurred within the first 15 m of the riparian zone at three sites with loamy sand and sandy loam soils overlying a shallow confining layer at 1–2 m. However, at four of five sites with more conductive sand and cobble sediments the width required for 90% nitrate removal varied from >25 m to a maximum of 176 m at a site with a confining layer at 6 m. Sites linked to an extensive thick (>6 m) upland aquifer with a slope gradient of >15% at the riparian perimeter had high nitrate inputs throughout the year and were large nitrate sinks. Sites with gentle topography (<4–5%) and <2 m of permeable sediments were minor nitrate sinks because of small nitrate inputs that were limited to the late autumn‐spring period. A conceptual model linking landscape hydrogeologic characteristics to riparian zone nitrate removal capacity is developed to understand and predict the effectiveness of riparian buffers at the landscape scale.
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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 | 0.001 |
| 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".