Towards silvicultural mitigation of the European ash (<i>Fraxinus excelsior</i>) dieback: the importance of acclimated trees in retention forestry
Bibliographic record
Abstract
The European ash (Fraxinus excelsior L.) dieback is an acute forest pathology problem caused by the invasive ascomycete Hymenoscyphus fraxineus (T. Kowalski) Baral, Queloz, Hosoya. There are no practical solutions yet, but selection for resistant genotypes and intensive care have been highlighted as options. Our aim was to assess the disease mitigation potential of silvicultural harvests, which influence stress levels in retained trees. We annually monitored 577 retention trees on Estonian cut areas for 13 years, including 9 years impacted by the dieback. Sixty-five percent of the trees survived and 15% retained healthy crown, despite all sampled trees being infected. The damage was smallest in the trees retained near precut edges. Former forest-interior trees that were left in central parts of the cut areas suffered high initial damage but smaller disease progression than trees near postcut edges. Tree size and secondary infection by Armillaria spp. were not related to disease progression, but rapid decline was observed in the region with the highest density of ash trees retained. Our results indicate a significant silvicultural potential for tree resistance. Ash trees tend to be healthiest in open conditions, which probably inhibit the pathogen and provide better resources for the tree. Location near precut edges is an important tree retention criterion, which can mitigate initial harvest-induced stress.
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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 | 0.001 | 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".