Fagus grandifolia growth and mortality a decade after the emergence of Beech leaf disease
Bibliographic record
Abstract
Beech leaf disease (BLD) is poised to cause major declines in American beech ( Fagus grandifolia ) across the eastern United States and parts of Canada. Given the dominance of this tree, quantifying impacts of this emerging disease is critical. Using long-term data from an existing field experiment (originally established to explore the impacts of soil chemistry on forests), we quantify rates of mortality and growth in American beech afflicted with BLD near the disease's epicenter. Since the initial observation of BLD in Cuyahoga County, OH in 2014, 75 of the 263 (29 %) American beech trees within our study have died. Most of this mortality was recent, with 2021–2023 displaying the highest levels of beech mortality (56 trees dying across the three years). Mortality was distributed unevenly across size classes, with the highest rates of mortality occurring in trees <25 cm DBH. Mortality rates were influenced by beech abundance, suggesting that areas with high concentrations of beech may experience higher rates of mortality. In addition, beech grew more slowly in recent years (2017–2022), suggesting a probable slowing of growth rates associated with BLD. Further, we observed lower growth rates in plots with the addition of soil amendments, but only before the arrival of BLD. As a dominant tree in many forests, this decline in American beech could catalyze larger stand-level changes in forest composition and function as BLD persists on the landscape and continues to spread into new areas.
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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 | 0.000 | 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) | 0.001 | 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".