Community and population dynamics of sprucefir forests on Whiteface Mountain, New York: recent trends, 19852000
Bibliographic record
Abstract
We remeasured two sets of permanent plots in old-growth, sprucefir forests on Whiteface Mountain to quantify ongoing vegetation dynamics at sites impacted by spruce decline. One set of plots was a stratified random sample of the vegetation in a subalpine watershed (Baldwin site). The other was selected to represent forest conditions in a high-elevation subset of the sprucefir forest (Esther site). Between 1987 and 1997, there was a significant increase in aboveground tree biomass at Baldwin with the majority of the increment due to the growth of canopy-sized trees. This growth occurred with little change in either species composition or size structure. The annual mortality rate of 1.2%·year1 for canopy-sized red spruce (Picea rubens Sarg.) in Baldwin almost matched the recruitment rate of 1.4 stems/ha per year. In addition, the relative growth rate of spruce was significantly faster than associated species. In contrast, spruce trees in Esther died at a rate of the 3.6%·year1 (19851995), and survivors grew more slowly than other species. The most obvious community-level trend at Esther (19852000) was an increase in overall tree density with most of this increase due to ingrowth of small trees. The demography of the spruce population at Baldwin suggests that the decline is over for at least this population.
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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 | 0.000 | 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) | 0.001 | 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".