Использование молекулярных методов селекции на устойчивость к сетчатой пятнистости ячменя (обзор)
Bibliographic record
Abstract
In recent years, due to the increasing aridity there was an increase in the frequency of epiphytoties leaf disease of barley. Therefore, we can see now how greatly increased the need for the creation of resistant grades. Introduction of the selection marker will significantly speed up the breeding process for resistance to pathogens. Employees of the All-Russian Institute of Plant Protection recommended three genes - Rpt 1b, Rpt 5, Rpt 6 to control the resistance to the pathogen Pyrenophora teres for use in breeding programs in the North Caucasus. Rpt 5 gene is one of the most efficient one, because it determines the resistance to the eight isolates of the fungus Pyrenophora teres of different origin. In 2011, Australian scientists have discovered microsatellite markers called Bmag0173 and HVM74, closely linked to the gene Rpt5, which are used very effectively in order to marker-assisted selection of resistant grades in Australia and Canada. Due to the urgency of creating new genotypes carrying resistance genes to net blotch, that would be effective in the Southern Federal Region, the staff of All-Russian Scientific-research institute of Grain Crops after I.G. Kalinenko and the All-Russian Research Institute of Plant after N.I. Vavilov has developed a practical strategy for the use of molecular techniques to create barley grades resistant to this pathogen, which is being successfully implemented
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.033 | 0.014 |
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".