Bibliographic record
Abstract
An assessment was made of stem rust race analysis on a global scale. Responses were obtained from 23 rust workers representing 21 countries. Five laboratories have an institutional history in stem rust race analysis of more than 60 years, whereas personal experience in this field ranged from 0 to 35 years. The number of stem rust samples processed from 2006 to 2008 varied greatly between countries. For the three year period most collections were characterized in Canada, followed by Georgia, USA, South Africa and Australia. Most laboratories use the North American differential set and nomenclature system. However, these entries are often supplemented by additional tester lines from the Stakman set, other single gene lines or local cultivars. Differential sets varied between eight and 50 entries. More than half of the respondents indicated that they often encounter seed mixtures amongst their differentiating lines. In recent surveys most races were detected in Ethiopia, followed by Georgia and China. One race dominated the USA and Canadian stem rust population. In South America and Australia stem rust has been rare in commercial wheat for many years. Races within the Ug99 cluster were frequently identified in stem rust collections from Kenya and Ethiopia. Two races related to Ug99, but avirulent on Sr31, occur in South Africa. Several laboratories are in the process of purifying and bulking differential seed, which appears to be one of the major limiting factors in reliable stem rust race analysis. Improvement of infrastructure and training of individuals inexperienced with stem rust should improve global surveillance efforts. In addition, countries doing race analysis should keep viable culture collections in long-term storage.
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.
How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".