Beech leaf disease: An emerging forest epidemic
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it