A framework to evaluate climate effects on forest tree diseases
Bibliographic record
Abstract
Abstract A conceptual framework for evaluation of climate effects on tree diseases is presented. Climate can exacerbate tree diseases by favouring pathogen biology, including reproduction and infection processes. Climatic conditions can also cause abiotic disease—direct stress or mortality when trees’ physiological limits are exceeded. When stress is sublethal, weakened trees may subsequently be killed by secondary organisms. To demonstrate climate's involvement in disease, associations between climatic conditions and disease expression provide the primary evidence of atmospheric involvement because experimentation is often impractical for mature trees. This framework tests spatial and temporal relationships of climate and disease at several scales to document climate effects, if any. The presence and absence of the disease can be contrasted with climate data and models at geographic scales: stand, regional and species range. Temporal variation in weather, climate and climate change is examined during onset, development and remission of the disease. Predisposing factors such as site and stand conditions can modify the climate effects of some diseases, especially at finer spatial scales. Spatially explicit climate models that display temperature and precipitation or derivative models such as snow and drought stress provide useful data, and however, information on disease extent at different spatial scales and monitoring through time are often incomplete. The framework can be used to overcome limitations in other disease causality approaches, such as Koch's postulates, and allow for the integration of vegetation, pathogen and environmental data into causality determinations.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".