Virulence characterization of <i>Puccinia striiformis</i> f. sp. <i>tritici</i> collections from six countries in 2013 to 2020
Bibliographic record
Abstract
Puccinia striiformis f. sp. tritici (Pst) causes wheat stripe rust (also called yellow rust, Yr), one of the most important diseases worldwide. Characterization of virulence in Pst populations is essential for developing wheat cultivars with effective and durable resistance to control the disease. A total of 138 Pst races, including 120 races that were not previously reported, were identified from stripe rust collections made from Canada, China, Ecuador, Egypt, Italy and Mexico in 2013–2020 using a set of 18 Yr single-gene differentials. Virulence of the resistance gene Yr5 or Yr15 was not found in isolates from any of the countries, indicating their effectiveness against the Pst populations. Virulence to 16 Yr genes was detected, but the frequencies varied greatly among countries. On average, the frequencies of virulence to Yr6, Yr7, Yr9, Yr43, Yr44 and YrExp2 were high (81.7–90.6%), those to Yr1, Yr8, Yr17, Yr27, YrSP and Yr76 were moderate (34.0–56.8%), and those to Yr10, Yr24, Yr32 and YrTr1 were low or very low (0.4–18.5%). The same races detected in different countries and different races from different countries clustered into the same virulence groups, indicating Pst migration among different countries, especially between eastern Asia and the Mediterranean region. These results should be useful for breeding wheat cultivars with effective resistance to stripe rust in these countries as well as globally.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".