Forest dynamics and the growth decline of red spruce and sugar maple on Bolton Mountain, Vermont: a comparison of modeling methods
Bibliographic record
Abstract
Montane forests in the northeastern United States have experienced symptoms of declining vigor, such as branch dieback and increased mortality, over the last half-century. These declines have been attributed to the cumulative impacts of acid deposition, but reconstructing these declines from tree-ring records has proved difficult because of confounding factors that affect low-frequency growth patterns, including climate and natural growth trajectories following disturbance. We obtained tree-ring records of red spruce ( Picea rubens Sarg.) and sugar maple ( Acer saccharum L.) from three elevations on Bolton Mountain, Vermont, and applied traditional dendroclimatological analyses that revealed a profound declining growth–climate correlation since ca. 1970 for sugar maple but much less so for red spruce. We then applied a new multifaceted statistical approach that conservatively detrends tree-ring records by minimizing the influences of tree size, age, and canopy disturbances on radial growth. In contrast with the traditional analysis, this approach yielded chronologies that were consistently correlated with climate but with important exceptions. Low-elevation sugar maple suffered distinct episodes of slow growth, likely because of insect defoliators, and also a progressive decline since ca. 1988. Red spruce experienced subdecadal episodes of decline that may be related to freeze–thaw events known to injure foliage but showed no evidence of a progressive decline. This analysis was supported by a forest plot resurvey that indicated major declines in these species.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".