Nitrate Dynamics in Relation to Lithology and Hydrologic Flow Path in a River Riparian Zone
Why this work is in the frame
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Bibliographic record
Abstract
Abstract The efficiency with which riparian zones remove nitrate (NO − 3 ) from contaminated ground water can vary with landscape setting. This study was conducted to determine the influence of flood plain geometry, lithology, hydrologic flow path, and nitrate transport on mechanisms of nitrate depletion of contaminated ground water. Patterns of NO − 3 −N, chloride, and dissolved organic carbon (DOC) concentrations and δ 15 N‐NO − 3 and δ 18 O‐NO − 3 values in combination with detailed piezometric head measurements were investigated in a river floodplain connected to a large upland sand aquifer in an agricultural region near Alliston, Ontario, Canada. Ground water discharging to the forested floodplain from the sand aquifer exhibited large spatial variability in NO − 3 −N concentrations (10–50 mg/L). The transport and depletion of NO − 3 was strongly influenced by floodplain geometry and lithology. Little ground water flow occurred through the low‐conductivity matrix of peat in the floodplain. Plumes of NO − 3 ‐rich ground water passed beneath the riparian wetland peat and flowed laterally in a 2‐ to 4‐m‐thick zone of permeable sands across the floodplain to the river. Analyses of the distribution of the NO − 3 −N concentrations, isotopes, and DOC within the floodplain indicate that denitrification occurred within the sand aquifer near the river where nitrate‐rich ground water interacted with buried channel sediments and surface water recharged from peat to the deeper sands. This study shows that the depth of permeable riparian sediments, ground water flow path, and the location of organic‐rich subsurface deposits may be more important than the width of vegetated strips in influencing the ability of riparian zones to remove nitrate.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 | 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 it