Genetic Analysis of Adult-Plant Resistance to Leaf Rust in Five Spring Wheat Genotypes
Bibliographic record
Abstract
Inheritance of adult-plant resistance to leaf rust, caused by Puccinia triticina, was studied in the progeny of a one-way diallel cross involving five CIMMYT-derived adult-plant resistant wheat (Triticum aestivum) genotypes and a susceptible wheat ‘Avocet-YrA’. F1 progenies, F2 populations, F2-derived F3, and F4-derived F5 lines were field evaluated under artificial epidemics with leaf rust race MCJ/SP. Adult-plant resistance to leaf rust was incompletely dominant in crosses with the susceptible parent and was found to be controlled by additive interactions of Lr34 with at least two to three additional genes. Transgressive segregation giving rise to plants or lines with higher and lower levels of resistance than the parents was observed in all F2 and F5 derivatives of the resistant-parent intercrosses and suggested that, apart from Lr34, some of the other additive genes were nonallelic. Although specific combining ability was significant in some generations, general combining ability was found to be the major component of variation. Among generations, the estimates of the narrow-sense heritability of adult-plant resistance to leaf rust ranged from 0.67 to 0.97.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".