Physiologic specialization of <i>Puccinia triticina</i> , the causal agent of wheat leaf rust, in Canada in 2020–2023
Bibliographic record
Abstract
Wheat nurseries and fields throughout Canada were surveyed annually, from 2020 to 2023, for the presence of leaf rust, caused by Puccinia triticina Erikss. Infected leaves were collected, then single pustule isolates were analyzed for virulence on a set of 16 differential lines. In 2020 there were 29 unique virulence phenotypes found among 230 isolates, in 2021 there were 37 unique virulence phenotypes found from 153 isolates, in 2022, 47 unique phenotypes were found among 246 isolates, and there were 69 unique phenotypes among 384 isolates in 2023. The most common virulence phenotypes over this 4-year period from Manitoba and Saskatchewan were MNPS, TNBJ, MBDS and MLPS. In Ontario these were TCTS, MBTN and TBRD, while in Quebec these were MBTN, TCTS, MBPS and TBSJ. The smaller samples from Ontario and Quebec were more diverse than the larger samples from Manitoba and Saskatchewan. There were only 25 isolates analyzed from British Columbia, but four of the five unique virulence phenotypes found there, LBDS, LCDS, CCPN and NBDS, were not found in the rest of Canada during this period. Only eight isolates from Alberta were analyzed and they were similar to virulence phenotypes found in Manitoba and Saskatchewan. The frequencies of virulence to Lr9, Lr24 and Lr21 were higher in Manitoba and Saskatchewan than in Ontario and Quebec, though the reverse was true for Lr2a, Lr2c and Lr18. When representative isolates were tested on additional differential lines there was no virulence detected to Lr19, Lr29, Lr32, Lr52 and Lr22a.
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 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 | 0.000 |
| 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) | 0.001 | 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".