Tree growth, foliar chemistry, and nitrogen cycling across a nitrogen deposition gradient in southern Appalachian deciduous forests
Bibliographic record
Abstract
The declining health of high-elevation red spruce (Picea rubens Sarg.) and Fraser fir (Abies fraseri (Pursh) Poir.) in the southern Appalachian region has long been linked to nitrogen (N) deposition. Recently, N deposition has also been proposed as a source of negative health impacts in lower elevation deciduous forests. In 1998 we established 46 plots on six sites in North Carolina and Virginia dominated by American beech (Fagus grandifolia Ehrh.), sugar maple (Acer saccharum Marsh.), and yellow birch (Betula alleghaniensis Britt). We evaluated several response variables across an N deposition gradient, including annual basal area growth; foliage percent N, Al, P, K, Mg, and Ca; and forest floor percent N, Mg, and C, pH, and potential net nitrification and N mineralization rates. We found a significant linear relationship between N deposition and basal area growth in sugar maple, but not in American beech or yellow birch. In addition, we found a significant relationship between N deposition and foliar chemistry (foliar %N in all species, foliar Mg/N and %K in sugar maple, and %P in sugar maple and yellow birch). Foliar %N of the three studied species was high relative to values reported in other studies in the United States and Canada. Several forest floor response variables (%N, C/N, pH, Mg/N, and potential net nitrification and N mineralization rates and nitrification/mineralization fractions) were also correlated with N deposition. The correlations between the above response variables and N deposition are consistent with the influence of chronic N deposition on forested ecosystems measured in other regions and suggest that chronic N deposition may be influencing forest structure and chemistry within the southern region.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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 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".