Gremmeniella abietina: a Loser in the Warmer World or Still a Threat to Forestry?
Bibliographic record
Abstract
Abstract Purpose of Review Gremmeniella abietinais a destructive forest pathogen responsible for Scleroderris canker, shoot dieback, defoliation, and tree death in forests and tree nurseries. This review is aimed at providing a complete description of the fungus, its distribution, the conditions for its spread, and the impact of climate change and at summarising the relevant forest management methods. Due to the worldwide importance of the pathogen, a retrospective review is required to summarise the lessons learned in relation to the disease, considering application to future outbreaks. Recent Findings We revise available management methods, considering examples of control strategies, with special focus on the silvicultural approaches, and we also revise the recovery of the affected stands and the associated trade-offs. Forest disturbances such as pests and disease outbreaks are expected to be exacerbated by climate change, although the exact impact on all host-pathogen interactions remains unclear. In regions with a high risk ofG. abietinaepidemics, climate change is expected to affect the pathogen differently. Summary Gremmeniella abietinais a widely distributed forest pathogen in Europe and is also present in North America. Based on the conclusions reached in this review, forest stands may recover from pathogen outbreaks within 10 years, with considerable loss of growth and the risk of attack from secondary factors. Provenance selection is vital for preventing outbreaks. Climate change is expected to have different effects: in some areas, it is likely to increase the conditions conducive to the development of the fungus, while in others, it is likely to limit the spread because of high temperatures and low humidity. Preventing future outbreaks of this pathogen requires the use of mitigating strategies, together with forest monitoring, forecasting, and planning.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.002 | 0.001 |
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".