Results of studying wheatgrass (AgropyronGaertn.) accessions from the VIR global genetic resources collection in Yakutia
Bibliographic record
Abstract
With the globally changing climate, researchers seek to use plants adapted to extreme environments in breeding and genetic programs. As a forage crop, the wheatgrass from the genus AgropyronGaertn. is most suitable for such purposes. The area of temperature distribution for wheatgrass lies in the range from +42 (arid zone) to –60°C (Verkhoyansk). The use of different wheatgrass species as a crop for arid conditions is quite profoundly studied in the USA, Canada, Russia, and Kazakhstan. Genetic and breeding studies are underway in different countries. In Yakutia, with its extremely continental climate, wheatgrass has not yet been introduced as a crop, although it could play an important role in establishing a sustainable fodder reserve. The aim of the work was to study and select promising breeding source material, identifying germplasm with the best agronomic traits. As a result of a two-year study of 19 accessions of different wheatgrass species from the collection of VIR, undertaken in 2018 and 2019 in the collection nursery in Central Yakutia, plant forms were selected that exceeded the average green biomass yield for two cuts: k-52382 (wild crested wheatgrass, Pavlodar Region, Kazakhstan) by 43%, and k-48705 (wild-growing Kerch wheatgrass) by 40%. Besides, wild wheatgrass accession k-52382 was identified for its dry matter yield (40.2% higher than the average) and for the total green and dry matter yield for the two cuts (212.7 g/plant).Accessions k-52440 (wild Siberian wheatgrass, Stavropol Territory) and k-51330 (crested wheatgrass, Chelyabinsk Province) were selected for their high seed yield (43.5 g/m² and 41.7 g/m², respectively). The content of crude and digestible protein was the highest in k-50857 (crested wheatgrass cv. ‘Ephraim’, USA) and k-50858 (Siberian wheatgrass cv. ‘Vavilov II’, USA): 14.6% and 99 g/kg of feed, and 14.2% and 96 g/kg of feed, respectively. Winter hardiness of 12 accessions turned out to be 100%, with 80% in another 7 accessions.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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".