Particulate nitrogen exports in stream runoff exceed dissolved nitrogen forms during large tropical storms in a temperate, headwater, forested watershed
Bibliographic record
Abstract
Abstract Although lasting only a fraction of the year, large storms may represent a significant, but highly variable, control on watershed nitrogen (N) fluxes. We determined the exports of particulate N (PN) and total dissolved N including nitrate‐N (NO 3 ‐N) and dissolved organic N (DON) in streamflow from a 12 ha temperate forested watershed. Sampling was performed for 15 storms over September 2010 to December 2012 and included four large tropical storms—Nicole (2010), Irene and Lee (2011), and Sandy (2012). PN composed a substantial portion (39–87%) of the storm event N export with storms constituting 65% of the 2011 PN export. Tropical storm Irene alone generated 1.76 kg N ha −1 of PN which was 27% of the annual watershed N (6.43 kg N ha −1 ) export for 2011. In contrast, tropical storm Sandy (October 2012) yielded low sediment and PN exports, likely due to low precipitation intensity and a freshly fallen leaf cover that may have reduced soil erosion. Stream water concentrations of PN, NO 3 ‐N, and DON ranged from 0–17.5, 0–2.02, and 0.01–0.54 mg N L −1 , respectively. Nitrate‐N concentrations displayed a dilution trajectory for peak stormflows suggesting supply limitation, a response that was not as strong for PN. These results underscore the importance of large storms for PN export which is significant given that climate‐change predictions indicate an increasing intensity of large tropical storms for the northeast U.S.. Elevated PN exports could further exacerbate water quality and eutrophication problems in sensitive aquatic ecosystems already subjected to excess dissolved nitrogen loads.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".