Landscape Hydrogeology and its Influence on Patterns of Groundwater Flux and Nitrate Removal Efficiency in Riparian Buffers
Bibliographic record
Abstract
Abstract This study uses data from 46 riparian sites to examine the influence of landscape hydrogeology on patterns of groundwater flux and the buffer width required for effective nitrate removal in humid temperate agricultural regions. There is a considerable imbalance in the research focus on different hydrogeologic settings. More than 40% of the buffers are located in landscapes with surficial sand aquifers, whereas few buffers have been studied in glacial till and weathered bedrock landscapes which cover large areas. Annual groundwater fluxes for 29 of these sites ranged from <20 L/m/day for buffers on flat sand plains and uplands with fine‐textured deposits to 50‐1,200 L/m/day for many sites with upland sand aquifers. Despite a similar range of water fluxes, buffers in gently to moderately sloping landscapes with <4 m depths of sand sediments reached a 90% removal efficiency within 30‐60 m while sites with >4 m depths required a 150‐200 m width. The width for 90% efficiency in buffers with loamy sand and sandy loam sediments also increased from 10‐20 m with <4 m sediment depths to 50‐100 m for >4 m depths. Limited data for buffers with fine‐textured sediments suggest that 90% of the nitrate flux was often depleted in a 10‐20 m width. Groundwater flux did not have a significant relationship with nitrate removal percent per meter buffer width because of the variation in efficiency that occurred in buffers with similar fluxes in different hydrogeologic settings.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".