Physiological specialization of <i>Puccinia triticina</i>, the causal agent of wheat leaf rust, in Canada in 2013
Bibliographic record
Abstract
Wheat leaves infected with leaf rust collected across Canada in 2013 were used to isolate 265 Puccinia triticina Eriks. single uredinial isolates. When these were analysed for virulence on 16 standard differential wheat lines 38 virulence phenotypes were found, with MBDS (11.7%), TBBG (11.3%), TNBG (10.2%) and MBTN (8.3%) the most common. In Manitoba and Saskatchewan, 29 virulence phenotypes were found among 236 isolates, with MBDS (13.1%), TBBG (12.7%) and TNBG (11.4%) being the most common. From Ontario, four virulence phenotypes MBTN (73.7%), LCDN (10.5%), MGPS (10.5%) and TCRJ (5.3%) were determined among 19 isolates. There were 10 isolates from Prince Edward Island which grouped into seven different virulence phenotypes, the most common being MBNQ (three isolates) and MCNQ (two isolates). The frequencies of virulence to Lr9, Lr26, Lr3ka, Lr17, Lr30, Lr14a and Lr21 increased, and virulence to Lr2a, Lr2c, Lr16, Lr24, Lr11, Lr10 and Lr18 decreased, when compared with 2012. The increase in virulence frequency to Lr21 is important since many Canadian wheat cultivars have this gene, and could become more susceptible. There were no virulence phenotypes in common between Ontario and Prince Edward Island, and only one virulence phenotype from each of these regions was found in the larger sample from Manitoba and Saskatchewan, demonstrating the differences in the populations across Canada.
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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".