Impact of winter warming on the timing of nutrient export from forested catchments
Bibliographic record
Abstract
Abstract Winter climatic conditions can influence the timing and magnitude of water and nitrate (NO3–N) export from seasonally snow‐covered catchments. Specifically, mid‐winter rain‐on‐snow (ROS) events are a major source of NO3–N export to forested streams, but the impact of these events on other nutrients is not known. Climate projections for Ontario suggest that climate warming will be most pronounced during the winter months, which could result in more mid‐winter rain events and consequent changes in nutrient delivery to streams. The objective of this study was to examine the impact of winter climate variability on the timing of NO3–N export relative to water and other nutrients at six headwater catchments in south‐central Ontario that have long‐term water quality and hydrology records (1980–2002). The catchments represent a wide range of physiographic characteristics and stream chemistry, yet the timing of nitrate export from all catchments was coherent. In warmer winters with more ROS events, the bulk of NO3–N export relative to the export of water shifted earlier in the year from spring (i.e. the main period of snow melt) to winter. ROS events did not cause similar temporal shifts in the export of other nutrients, including dissolved organic carbon, total phosphorus and calcium. Instead, their export was synchronous with the bulk of water export. Future shifts to earlier export of NO3–N relative to water and other nutrients may impact aquatic productivity and cause more frequent episodic acidification of surface waters. Copyright © 2012 John Wiley & Sons, Ltd.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 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".