Climate change and plant diseases in Ontario
Bibliographic record
Abstract
Current models predict that expected climate change in Ontario will significantly affect the occurrence of plant diseases in agriculture and forestry in the coming years. Direct, multiple effects on the epidemiology of plant diseases are expected, including the survival of primary inoculum, the rate of disease progress during a growing season, and the duration of epidemics. These effects will positively or negatively influence individual pathogens and the diseases they cause. Changes in the spectra of diseases are also anticipated. Abiotic diseases associated with environmental extremes are expected to increase, and interactions between biotic and abiotic diseases might represent the most important effects of climate change on plant diseases. The management of plant diseases will also be affected. In agriculture, plant breeding programs are expected to adapt many crops to increased duration of growing seasons and, concurrently, to develop drought and stress tolerance. There will be opportunities for new crops and cultivars to be introduced, but effective systems must be in place to detect new pathogens and prevent them from entering with these new crops. Because of the long-lived nature of trees, forests are slow to adapt, and the impact of climate change will have to be considered in forest management plans. Adaptations in agriculture and forestry have been occurring in Ontario for over 100 years, but these may need to occur at an accelerated rate because of rapid changes in climate. It is critical that the infrastructure of agricultural and forestry research remains strong to ensure successful transition and adaptation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 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) | 0.004 | 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".