The contribution of rain‐on‐snow events to nitrate export in the forested landscape of south‐central Ontario, Canada
Bibliographic record
Abstract
Abstract Rain‐on‐snow (ROS) events have the potential to contribute significantly to nitrate (NO 3 ‐N) export from forested catchments, but have received relatively little research attention. This study assesses the importance of ROS events for NO 3 ‐N export across 18 catchments in south‐central Ontario, Canada, that receive the same annual and seasonal N deposition, but encompass a range of physiographic characteristics. Winter (December to February) NO 3 ‐N export was calculated from 1982 to 1987, a period when streams were sampled on average every 3·3 days for NO 3 ‐N analysis. ROS events contributed a similar proportion of total winter NO 3 ‐N export across all catchments (median proportion of NO 3 ‐N from ROS events = 55%). There was considerable variation in the total magnitude of winter NO 3 ‐N export from these catchments, ranging from 0·01 to 0·4 kg/ha. Analysis of relationships between NO 3 ‐N export and physiographic characteristics indicated that NO 3 ‐N export varied with till coverage, wetland coverage and slope. Catchments with more till coverage, less wetland coverage and steeper slopes may be able to sustain hydrological linkages with the stream channel during the winter, contributing to higher NO 3 ‐N export. An analysis of one catchment over a longer time period (1976–2001) revealed that years with higher maximum winter temperatures had more ROS events than cooler winters ( p < 0·001; r 2 = 0·48). As climate projections for this region include increased winter temperatures and more winter precipitation falling as rain, ROS events may increase in the future, raising concerns about increased NO 3 ‐N loading to surface waters. Copyright © 2010 John Wiley & Sons, Ltd.
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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 | 0.000 | 0.001 |
| 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 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".