Inheritance and QTL analysis of durable resistance to stripe and leaf rusts in an Australian cultivar,<i>Triticum aestivum</i>'Cook'
Bibliographic record
Abstract
An F4-derived F6 recombinant inbred line population (n = 148) of a cross between the durable stripe (yellow) rust (caused by Puccinia striiformis) and leaf (brown) rust (caused by Puccinia triticina) resistant cultivar, Triticum aestivum 'Cook', and susceptible genotype Avocet-YrA was phenotyped at several locations in Canada and Mexico under artificial epidemics of leaf or stripe rusts and genotyped using amplified fragment length polymorphism (AFLP) and microsatellite markers. Durable adult plant resistance to stripe and leaf rusts in 'Cook' is inherited quantitatively and was based on the additive interaction of linked and (or) pleiotropic slow-rusting genes Lr34 and Yr18 and the temperature-sensitive stripe rust resistance gene, YrCK, with additional genetic factors. Identified QTLs accounted for 18% to 31% of the phenotypic variation in leaf and stripe rust reactions, respectively. In accordance with the high phenotypic associations between leaf and stripe rust resistance, some of the identified QTLs appeared to be linked and (or) pleiotropic for both rusts across tests. Although a QTL was identified on chromosome 7D with significant effects on both rusts at some testing locations, it was not possible to refine the location of Lr34 or Yr18 because of the scarcity of markers in this region. The temperature-sensitive stripe rust resistance response, conditioned by the YrCK gene, significantly contributed to overall resistance to both rusts, indicating that this gene also had pleiotropic effects.
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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 | 0.000 | 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) | 0.001 | 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".