STUDYING GENETIC RESOURCES OF SPRING BREAD WHEAT IN THE ENVIRONMENTS OF NORTHERN KAZAKHSTAN
Bibliographic record
Abstract
Background. Spring bread wheat is the main export crop in Kazakhstan. Unfortunately, wheat varieties cultivated for large-scale production do not fully meet the requirements of agricultural producers. The world diversity of wheat genetic resources should be widely used in breeding programs in order to develop new wheat cultivars with stable yields and with resistance to adverse environmental factors. Materials and methods. One hundred collection accessions of spring bread wheat were studied in 2015–2017 at the A.I. Barayev Science and Production Center of Grain Farming, Ltd. Seeds were sown at an optimum time (May 20–25), using an SSFC-7 seeder. Harvesting was conducted with a Wintersteiger combine. The study of the collection material was carried out in accordance with the guidelines developed by the N.I. Vavilov Institute of Plant Genetic Resources (VIR). The protein content was measured in line with State Standard 10846-91. The method of sodium dodecyl sulfate (SDS) sedimentation, modified by V. M. Bebyakin and M. V. Buntina, was used to measure the level of sedimentation. Results and conclusion. During the three-year study of spring bread wheat accessions in Northern Kazakhstan, only the cultivars ‘Shortandinskaya 2012’ and ‘Astana 2’ exceeded the reference ‘Astana’ in yield. The accessions ‘BW 252’, ‘Neepawa’ (Canada), ‘MANITUOU LR 13’ (CIMMYT, Mexico) and ‘Novosibirskaya 29’ (Russia) ripened 1–2 days earlier than the reference, while their average yield for 3 years was almost on the same level with the reference. The cultivars ‘Astana’ (the reference, Kazakhstan), ‘WA007824 WA7824’ (USA), ‘Novosibirskaya 29’, ‘Novosibirskaya 15’ (Russia), ‘OPATA85 LR10’ and ‘LR27+LR31,LR34’ (CIMMYT, Mexico) were distinguished for grain quality due to their high protein content and the level of sedimentation.
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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 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".