American beech mortality in stands recently infected by beech bark disease: implications for partial cutting
Bibliographic record
Abstract
Beech bark disease is a major concern for northern hardwood forest management that affects most of the American beech range in North America. In infected stands, mitigating effects of the disease and promoting more resistant beech populations for natural regeneration relies heavily on our ability to identify high-risk trees and adapt tree marking for partial harvesting. We monitored several individual characteristics, including external signs of disease, on 871 beech trees in recently infected northern hardwood stands at the northern range limit of American beech in Canada, to assess their ability to predict mortality over an 8-year period. At the stand level, the mortality rate over the study period was 29.3%, while the uninfected rate was 16.6%. At the tree level, the diameter, the levels of Neonectria perithecia coverage on the stem, crown dieback, and the level of canker coverage on the bark had the greatest capacity to predict individual short-term mortality. Therefore, tree markers should first select trees with a diameter > 20 cm that are affected by any sign or symptom of the disease, followed by smaller trees with >10% coverage of Neonectria perithecia or crown dieback >25%, and lastly, trees with >50% coverage of canker.
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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 | 0.001 | 0.002 |
| 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".