Challenges and opportunities of breeding and genetic improvement of durum wheat in Russia
Bibliographic record
Abstract
Favorable soil and climatic environments of Russia are not sufficiently used for the production of high-quality grain of durum wheat.It is caused by a lower efficiency of its cultivation compared to other cereals.The development of varieties adapted to environmental fluctuations in the zones of their cultivation, with high grain quality, is taken as one of the major factor to solve the problem.Based on many-year experiments a breeding strategy for adaptation is suggested.It roots in the possibility to reinforce specific (regional) homeostasis with the genetic systems of cultivars living on a vast area, which are carriers of non-specific homeostasis, as well to increase resistance to diseases (foliar blights, blotches, stem rust, powdery mildew) and to lodging.Ways to enhance grain quality due to the use of germplasm with high levels of protein, gluten and carotenoid content are put forward.Problems of strengthening gluten quality of Russian durum wheat cultivars are discussed.For these purposes, cultivars from Italy, Canada and Australia should be widely used as basic material and the corresponding biochemical markers of the GLi-B1d, Glu-B1d, Glu-A3d loci would be quite valuable.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".