Nitrogen deposition and forest expansion in the northern Great Plains
Bibliographic record
Abstract
Summary Atmospheric nitrogen (N) deposition has become one of the most important agents of vegetation change in densely populated regions. It may also contribute to forest expansion into grasslands at the northern edge of the North American Great Plains. We measured N deposition and available soil N with ion‐exchange resin over 2 years in six national parks in areas varying in population density and industrialization. N deposition was significantly higher in four parks in densely populated regions than in two remote parks. Available soil N increased significantly with N deposition across all parks. We measured N mass and 15 N abundance (δ 15 N) in vegetation and soil in two parks: Elk Island, receiving 22 kg N ha −1 year −1 , and Jasper, receiving 8 kg N ha −1 year −1 . Differences between parks in tissue N concentrations were small, but forest expansion over five decades resulted in the mass of N in vegetation increasing by 74% in Elk Island but by only 26% in Jasper. δ 15 N in forest vegetation was significantly lower in Elk Island than in Jasper, suggesting that anthropogenic sources contribute significantly to the high rates of N entering that ecosystem. We determined the rate of forest expansion within parks using six decades of aerial photographs. Parks in aspen parkland and boreal forest showed a strong positive relationship between forest expansion and N deposition. The relationships found between N deposition, available soil N and forest expansion suggest that even comparatively low rates of N deposition may accelerate the expansion of forest into temperate grasslands.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".