Virulence in populations of <i>Puccinia graminis</i> f.sp. <i>tritici</i> in Canada from 1952 to 1998: a non-parametric analysis
Bibliographic record
Abstract
Six hundred and twenty six isolates of Puccinia graminis f.sp. tritici that were collected and stored between 1952 and 1998 were identified using 32 single-gene differential wheat lines. These pathotypes represented isolates from field surveys, nursery collections, and from greenhouse experiments. Infection type data was converted to a binary data matrix with a 0 (resistant) or 1 (susceptible) numeral assigned to each isolate for each differential line. The Gower coefficient of similarity was determined for every pair of isolates, then they were clustered using the non-parametric cluster analysis MODECLUS. Eight significantly different clusters were obtained from an overall heterogeneous database of 405 unique pathotypes representing all regions of Canada. For further analysis, isolates obtained only from field survey collections were selected and divided by region of collection into Pacific (45 pathotypes), prairie (191 pathotypes), and eastern Canadian (83 pathotypes) populations. The Pacific population, which was both sexually and asexually reproducing, consisted of two clusters. The prairie population, strictly asexually reproducing, consisted of nine clusters, and the eastern population, which may be partially sexually reproducing, had three clusters. The Pacific population was shown to be significantly different from the prairie and eastern populations, while the prairie and eastern populations were less distinct. The pathotype composition of the regional clusters, reliability of cluster segregation using non-parametric analysis, and usefulness of the data to contribute to a revised nomenclature of P. graminis f.sp. tritici, are evaluated.Key words: stem rust, black rust, wheat, specific virulence.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".