Climate change and plant diseases in Ontario
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
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.
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) | 0.000 | 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