Variation in overstory nitrogen uptake in a small, high-elevation southern Appalachian spruce-fir watershed
Bibliographic record
Abstract
High-elevation red spruce (Picea rubens Sarg.) Fraser fir (Abies fraseri (Pursh) Poir.) forests of the southern Appalachians exhibit considerable spatial heterogeneity in structure, and possibly in N uptake, because of a combination of natural disturbances and heavy fir mortality caused by infestations of the exotic balsam woolly adelgid (Adelges piceae Ratz). The objectives of this study are to determine spatial variability in tree N uptake in a small high-elevation catchment in the Great Smoky Mountains National Park, compare outcomes among calculation methods, and assess the influence of stand and landscape properties on N uptake. Tree N uptake is estimated for fifty 20 × 20 m plots in the Noland Divide Watershed (NDW). Components considered in the calculations are stem growth, foliage increment, and mortality of spruce, fir, and yellow birch (Betula alleghaniensis Britt.) from 1993 and 1998 stand inventories; throughfall N flux measured in summers 1998 and 1999; litterfall N return for 1 year in a subset of 12 plots; tissue N analyses; and atmospheric N deposition and root turnover estimates from the literature. Overstory N uptake varies spatially within NDW, with a CV of 941% depending on the calculation method. Variability among methods is even higher, with an almost 15-fold difference between the smallest and largest average overstory uptake estimate (5 vs. 74 kg·ha1·year1). Only 5 and 3 kg·ha1·year1of N is sequestered in wood and foliar increment, respectively, while 36 kg·ha1of N returns annually as aboveground litterfall. Uptake and its components are correlated with measures of stand structure but not with elevation or aspect.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".