Evaluation of commercial traits in the accessions of the wheatgrass genus (<i>Agropyron</i> Gaertn.) under the conditions of Central Yakutia
Bibliographic record
Abstract
Studying plants of the wheatgrass genus as a unique and valuable fodder and phytomeliorating perennial arid xerophytic crop is of great interest to plant breeders, geneticists, biologists, ecologists, agriculturists, and forestry experts in southern regions of Russia, the ex-USSR republics, a number of European and Asia Minor countries, the U. S., Canada, and China. Accessions from the VIR collection representing five wheatgrass species were studied for the first time under the harsh conditions of extremely continental climate in the northern region of Central Yakutia. Introducing wheatgrass, widespread in this region, into cultivation, and releasing new cultivars adapted to local conditions are urgent tasks in forage production. Agropyron Gaertn. incorporates polyploid series, which expands the possibilities of using its accessions in hybridization. The aim of this study was to analyze and select promising accessions as sources for further use in breeding practice to develop a new cultivar for hay and pasture purposes, and identify genotypes with the best agronomic characteristics. Results of a three-year (2018–2020) study involving 22 wheatgrass accessions of various ecogeographic origin are presented. The accessions identified over a two-year period for their average yield of green fodder biomass were k-52382 (143.7 g/plant) from Pavlodar Region of Kazakhstan, and the Kazakh cultivar ‘Batyr’ (142.5 g/plant); for the yield of dry fodder biomass, crested wheatgrass k-52382 (on average 65.8 g/plant), k-51330 from Chelyabinsk Province (56.1 g/plant), and cv. ‘Batyr’ (53.2 g/plant); for high seed yield, Siberian wheatgrass accession k-52440 (28.4 g/m2), wild crested wheatgrass k-51330 (25.2 g/m2) and k-52380 (19.4 g/m2), and Kerch wheatgrass k-48705 (17.3 g/m2). Nutrients and energy in the tested accessions were assessed.
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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 | 0.001 |
| 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".