Dual nitrate isotopes clarify the role of biological processing and hydrologic flow paths on nitrogen cycling in subtropical low‐gradient watersheds
Bibliographic record
Abstract
Abstract Nitrogen (N) is an important nutrient as it often limits productivity but in excess can impair water quality. Most studies on watershed N cycling have occurred in upland forested catchments where snowmelt dominates N export; fewer studies have focused on low‐relief watersheds that lack snow. We examined watershed N cycling in three adjacent, low‐relief watersheds in the Upper Coastal Plain of the southeastern United States to better understand the role of hydrological flow paths and biological transformations of N at the watershed scale. Groundwater was the dominant source of nitrified N to stream water in two of the three watersheds, while atmospheric deposition comprised 28% of stream water nitrate in one watershed. The greater atmospheric contribution may have been due to the larger stream channel area relative to total watershed area or the dominance of shallow subsurface flow paths contributing to stream flow in this watershed. There was a positive relationship between temperature and stream water ammonium concentrations and a negative relationship between temperature and stream water nitrate concentrations in each watershed suggesting that N cycling processes (i.e., nitrification and denitrification) varied seasonally. However, there were no clear patterns in the importance of denitrification in different water pools possibly because a variety of factors (i.e., assimilatory uptake, dissimilatory uptake, and mixing) affected nitrate concentrations. Together, these results highlight the hydrological and biological controls on N cycling in low‐gradient watersheds and variability in N delivery flow paths among adjacent watersheds with similar physical characteristics.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".