Fungi insects and abiotic factors associated with the death of Euphorbia ingens in South Africa
Bibliographic record
Abstract
Globally, over the last 30 years, there has been an increase in the number of reports of tree mortality related to anthropogenically driven climate change. Changes in climate not only directly affect plant and tree growth but also influence insects and microbes (pests and pathogens) that interact with plants. Increased temperatures have, for example, led to an explosion in mountain pine beetle (Dendroctonus ponderosae) populations resulting in the death of more than 10 million hectares of Pinus contorta in Canada and the United States of America. This review considers the known and predicted impact of anthropogenic climate change on insects and pathogens in forest environments where large scale tree die-offs have been experienced. Most of these reports are from the Northern Hemisphere, but there are also instances in the Southern Hemisphere, including South Africa where tree die-offs are occurring and where climate is believed to play a role. For example, Euphorbia ingens trees in South Africa have been reported to be dying-off in unprecedented numbers. In this case, it has been suggested that opportunistic pests and pathogens, driven by changes in climate, may be contributing to the death of these trees. Climate change associated tree die-offs are not only of concern in the natural forest environment but are also important in planted forests where commercial impacts are relevant. Overall, climate change has become an important issue relating to tree diseases and it must be taken into consideration when investigating the factors involved in unexpected tree die-offs.
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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 | 0.001 |
| 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".