Physiological specialization of <i>Puccinia triticina</i>, the causal agent of wheat leaf rust, in Canada in 2014
Bibliographic record
Abstract
Wheat leaves infected with Puccinia triticina, the causal agent of wheat leaf rust, were collected from across Canada in 2014. From these leaves, 102 single-pustule isolates were recovered and tested for virulence on 16 standard differential wheat lines, with 28 unique virulence phenotypes found. The most common were TBDG (16.7%), TBBG (13.7%), MLPS and TNBG (both at 7.8%). Most isolates (95) originated from Manitoba and Saskatchewan, and the most common phenotypes found in this region were TBDG (17.9%), TBBG (14.7%), MLPS and TNBG (both at 8.4%). From Ontario, six virulence phenotypes were found from seven isolates: TCGJ (two isolates), MBDS, MCQG, MFPS, MGBS and TBGS (one isolate each). Frequencies of virulence in 2014 increased for Lr2a, Lr2c, Lr26 and Lr10 when compared with 2013, while there were decreases in virulence to Lr9, Lr24, Lr3ka, Lr30, Lr14a, Lr21 and LrCen. When 30 isolates, representing most of the unique virulence phenotypes, were tested on adult plants nearly all were virulent to the adult plant resistance genes Lr12, Lr13 and Lr37, most were avirulent to Lr35, and all were avirulent to Lr22a. When this subset of isolates was inoculated onto additional seedling differentials, most were virulent to Lr15 and Lr14b and avirulent to Lr25 and Lr29, and all isolates were avirulent to Lr19, Lr32 and Lr52. There were intermediate levels of virulence on Lr3bg, Lr20, Lr23 and Lr28.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 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".