АДАПТИВНОСТЬ СОРТОВ ЯРОВОЙ ПШЕНИЦЫ КРАСНОУФИМСКОГО СЕЛЕКЦЕНТРА И ИХ ЦЕННОСТЬ ДЛЯ СЕЛЕКЦИИ В ТЮМЕНСКОЙ ОБЛАСТИ
Bibliographic record
Abstract
The development of the first local mid-early variety of strong wheat Tyumenskaya 80 in the conditions of Northern Ural contributed to production of food grain in Tyumen region. Market relations require wheat varieties, which are efficient and resistant to the climate conditions. In order to solve this task it is necessary to have valuable starting material. The article explores the varieties of spring wheat cultivated in Krasnoufim selection centre. Vorobyev A.V. created scientific basis of cultivating spring wheat varieties with goof grain quality and crop yield. He used valuable genes of varieties from Canada, USA, Sweden, Norway and other foreign countries and national varieties. The varieties of Krasnoufim selection centre are well developed according to economic valuable features; they are widely spread and are very significant as a starting material for selection in other regions of the country. Many years research carried out at the Chair of Plant Breeding and Selection of Northern Ural Agrarian University show that the investigated varieties ripen at the same time as early ripening and mid-early ripening varieties of spring wheat. Having red ears of wheat, Krasnoufim spring wheat varieties ripened early than their standard varieties in wet and cold years and produced grain useful for baking industry. On the background of middle and high nutrition they have shown high plasticity and stability in quantitative features. Such red wheat ear varieties as Strela, Kometa, Irgina and Iren. Strela variety is characterized by high field emergence and crops survival for harvesting that is very important in Tyumen region. The authors include the investigated varieties into selection programmes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.008 |
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".