Forest pathogens with higher damage potential due to climate change in Europe
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 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 it