Two adjacent forested catchments: Dramatically different NO<sub>3</sub><sup>−</sup> export
Bibliographic record
Abstract
Two adjacent catchments with similar temperate forest cover and podzolic soils have annual nitrate (NO 3 − ) export that differs by a factor of 10. Monthly rates of mineralization and nitrification measured by the buried bag technique, soil C/N ratios, and the contribution of microbial NO 3 − to total NO 3 − in the groundwater as determined by analysis of δ 18 O in NO 3 − are also similar. In both catchments, maximum NO 3 − export occurs during spring melt, but in the catchment with higher export, NO 3 − concentrations in the stream begin to increase in the fall period. Groundwater NO 3 − concentrations measured in wells are very different in the two catchments with high groundwater NO 3 − in the catchment exhibiting high NO 3 − export. Following spring melt, steeper slopes in the high NO 3 − catchment promote faster drainage, and the water table declines rapidly while high NO 3 − concentrations are maintained in groundwaters. Deeper water tables will preserve high NO 3 − in water infiltrating below the rooting zone and organic‐rich upper soil horizons. In the low NO 3 − catchment, slower drainage on shallower slopes lead to an increase in soil saturation, and the NO 3 − disappears from the water before the water table declines. Analyses of δ 15 N in NO 3 − during NO 3 − loss do not show evidence of denitrification, although denitrification proceeding to completion in isolated pockets followed by mixing with higher NO 3 − groundwaters would yield the same result. Alternatively, active uptake of NO 3 − by vegetation following spring melt will also deplete the groundwater NO 3 − in the shallow soil depths without isotopic fractionation. The low NO 3 − catchment also has lower NO 3 − in shallow soil waters during spring melt. Shallower slopes promote near‐surface flow paths in organic‐rich soil horizons which may facilitate denitrification during spring melt. Although the catchment with low NO 3 − export has a large wetland near the catchment outlet, the NO 3 − attenuating capacity of this wetland is largely unused except in the late fall because growing season groundwater concentrations of NO 3 − are undetectable and the wetland is frozen during snowmelt. In the high NO 3 − catchment, organic‐rich soils and vegetation in the riparian zone cannot completely attenuate high NO 3 − in discharging groundwaters. In our study, factors controlling NO 3 − in groundwater such as slope, stratigraphy, and hydraulic conductivity can play a larger role than riparian zones in controlling differences in annual NO 3 − export observed between catchments.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.013 |
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; both teacher heads agree on what is shown here.
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".