Beech leaf disease: An emerging forest epidemic
Bibliographic record
Abstract
Abstract Beech leaf disease (BLD) is a currently undiagnosed and seemingly lethal disease that was discovered in 2012 on American beech trees (Fagus grandifolia) in north‐east Ohio in the United States. Since its discovery, BLD has spread rapidly and can now be found in forests in 10 counties in Ohio, eight counties in Pennsylvania and five counties in Ontario, Canada. The initial symptoms of the disease appear as a dark green, interveinal banding pattern on the lower canopy foliage. These initial symptoms typically occur in the shrub or sampling layer of a beech stand. The later symptoms result in solidly darkened leaves that are shrunken and crinkled. The symptoms appear to progress through the buds as the affected buds are eventually aborted and no new leaves are produced. We fear this disease has the potential to drastically alter the Eastern deciduous forests of the United States on its own and through potential compounding disease effects. In addition, BLD poses a threat to global forests as symptoms of the disease were detected on European (F. sylvatica) and Oriental (F. orientalis) beech species in nurseries in north‐eastern Ohio. Due to its rapid spread and variability in environmental conditions where it has been detected, it seems unlikely that BLD is an abiotic disorder. Thus, intense efforts are underway to determine the causal agent of BLD. Relevant stakeholders are advised to be alert for BLD symptoms in beech forests in the Northern Hemisphere, and substantial resources should be invested in understanding this emerging forest disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".