Development and validation of molecular markers linked to an<i>Aegilops umbellulata</i>–derived leaf-rust-resistance gene,<i>Lr9</i>, for marker-assisted selection in bread wheat
Bibliographic record
Abstract
An Aegilops umbellulata-derived leaf-rust-resistance gene, Lr9, was tagged with 3 random amplified polymorphic DNA (RAPD) markers, which mapped within 1.8 cM of gene Lr9 located on chromosome 6BL of wheat. The markers were identified in an F2 population segregating for leaf-rust resistance, which was generated from a cross between 2 near-isogenic lines that differed in the alien gene Lr9 in a widely adopted agronomic background of cultivar 'HD 2329'. Disease phenotyping was done in controlled environmental conditions by inoculating the population with the most virulent pathotype, 121 R63-1 of Puccinia triticina. One RAPD marker, S5550, located at a distance of 0.8+/-0.008 cM from the Lr9 locus, was converted to sequence-characterized amplified region (SCAR) marker SCS5550. The SCAR marker was validated for its specificity to gene Lr9 against 44 of the 50 known Lr genes and 10 wheat cultivars possessing the gene Lr9. Marker SCS5550 was used with another SCAR marker, SCS73719, previously identified as being linked to gene Lr24 on a segregating F2 population to select for genes Lr9 and Lr24, respectively, demonstrating the utility of the 2 markers in marker-assisted gene pyramiding for leaf-rust resistance in wheat.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.000 | 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".