Forest pathogens with higher damage potential due to climate change in Europe
Bibliographic record
Abstract
Most atmospheric scientists agree that climate changes are going to increase the mean temperature in Europe with increased frequency of climatic extremes, such as drought, floods, and storms. Under such conditions, there is high probability that forests will be subject to increased frequency and intensity of stress due to climatic extremes. Therefore, impacts of climate change on forest health should be carefully evaluated. Given these assumptions, several fungal diseases on trees may become more devastating because of the following factors: (i) abiotic stresses, such as drought and flooding, are known to predispose trees to several pathogens; (ii) temperature and moisture affect pathogen sporulation and dispersal, and changes in climatic conditions are likely to favour certain pathogens; (iii) migration of pathogens triggered by climatic change may increase disease incidence or geographical range, when pathogens encounter new hosts and (or) new potential vectors; and (iv) new threats may appear either because of a change in tree species composition or because of invasive species. If infection success is dependent on temperature, higher mean temperatures may lead to more attacks. Pathogens that have been of importance in southern Europe may spread northward and also upward to mountains. Pathogens with evolutionary potential for greater damage should be identified to estimate the magnitude of the threat and to prepare for the changing conditions. A review of the above-mentioned cases is presented. Some priorities to improve the ability to predict impacts of climate change on tree diseases are discussed.
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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 | 0.001 | 0.002 |
| 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".