Impact of climate change and race evolution on the epidemiology and ecology of stripe rust in central and eastern USA and Canada
Bibliographic record
Abstract
Stripe rust, caused by Puccinia striiformis f. sp. tritici (Pst), is one of the most devastating diseases of wheat globally. Since 2000, the geographic footprint of Pst has increased in North America due to a combination of changing weather patterns and introductions of new and more virulent strains. Pst is endemic throughout North America, but the emergence of new virulent strains poses an increased risk to wheat cultivation. As climate changes, so will the overwintering and over-summering range of this pathogen, resulting in earlier and more severe disease epidemics in the central and eastern USA and Canada. This perspective aims to highlight the changes taking place in Pst ecology and epidemiology over the last decade in North America, and then presents recommendations for future research to prepare for the changing dynamics of stripe rust. Specifically, to manage stripe rust in the future, molecular surveillance mechanisms need be established to identify and track new virulent races, and to obtain a deeper understanding of the adaptive ability of Pst. This will allow for a better explanation of how new races of Pst emerge, and encourage the development of improved epidemiological models and durable resistance strategies.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".