Application of molecular markers to wheat breeding in <scp>C</scp>anada
Bibliographic record
Abstract
Abstract Marker‐assisted breeding provides an opportunity for wheat breeders to introgress/pyramid genes of interest into breeding lines and to identify genes and/or quantitative trait loci in germplasm to be used as parents. Molecular markers were deployed to assist selection for disease resistance, agronomic and quality traits in several wheat cultivars released for commercial cultivation in Canada. Marker‐assisted breeding is routinely used in most wheat breeding programmes for rust resistance (leaf, stem and stripe rust), orange wheat blossom midge resistance, high grain protein concentration, Fusarium head blight and common bunt resistance. Markers are being used selectively within breeding programmes to target traits that relate to market class or regional adaptation. For example, marker‐assisted breeding for low lipoxygenase activity and low grain cadmium is being performed in durum breeding programmes and for enhancing stem solidness in programmes targeting resistance to the wheat stem sawfly. Markers are also being utilized for ergot resistance in durum wheat. Increased gluten strength is being selected with a marker for the overexpression of the Bx7 high‐molecular‐weight glutenin subunit. Marker‐assisted breeding is also being used to pyramid resistance genes against a group of stem rust races related to TTKS (Ug99), a disease that poses a serious threat to global wheat production. Development of tightly linked diagnostic markers and high‐throughput genotyping with SNP markers will result in more effective molecular wheat breeding in the near future and will open the door to genomic selection.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".