Physiologic specialization of <i>Puccinia triticina</i>, the causal agent of wheat leaf rust, in Canada in 2015–2019
Bibliographic record
Abstract
Wheat leaves infected with Puccinia triticina, the causal agent of wheat leaf rust, were collected annually throughout Canada from 2015 to 2019. There were 47, 75, 44, 38 and 54 different virulence phenotypes, respectively, found annually, representing 154 unique virulence phenotypes. From Alberta, there were three virulence phenotypes, MBDS, TDBJ, and TDBS, found in 2015 and eight in 2019, the most common being MBDS and TNBJ (both 20%). The most common virulence phenotypes found in Manitoba and Saskatchewan were MBDS (17.8%) and TNBG (16.3%) in 2015, MNPS (17.2%) and MBDS (15.9%) in 2016, MNPS (41.9%) and TBBG (17.2%) in 2017, MNPS (35.3%) and TBBG (34.8%) in 2018, and MNPS (54.7%) and MBDS (10.7%) in 2019. In Ontario, the most common virulence phenotypes found were MBTN (25%) and MBDS (16.7%) in 2015, MCQQ (13.6%) in 2016, MBTN (33.3%) and PBDG (19.0%) in 2017, TFPJ (13.6%) in 2018, and MCTN (14.5%) and MBTN (10.9%) in 2019. In Quebec the most common virulence phenotypes found were TBBG (66.7%) and MLDS (33.3%) in 2015, TCGJ (23.1%) and MBTN (15.4%) in 2017, MCQH (26.3%) and FCPT (21.1%) in 2018, and MBTN and MCRS (both 18.2%) in 2019. The frequencies of virulence varied on all resistance genes over these years. Within Canada, virulence on Lr21 peaked in 2018 at 39.9% and then declined in 2019, with a similar trend noticed for virulence on Lr2a and Lr2c. There was no virulence detected on Lr19, Lr29, Lr32, Lr52, and Lr22a, while virulence on Lr25 was rare.
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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".