Source material for breeding winter bread wheat for grain quality in the north of the Middle Volga Region
Bibliographic record
Abstract
Background. Development of cultivars with high stable yields and high grain quality is the main trend in wheat breeding. The aim of this study was to characterize a set of winter bread wheat accessions from the VIR collection and the working collection of Kazan Scientific Center in terms of their yield, protein content in grain (P, %), and swelling of flour in acetic acid (S, ml), and select the best accessions for the combination of these characters for use in a crossbreeding program.Materials and methods. Twenty-three winter bread wheat accessions were studied for the abovementioned characters in the north of the Middle Volga Region using conventional techniques. The study lasted three years (2016–2019).Results and conclusion. The yield of the accessions varied across the years of studies; however, none of them surpassed the reference cv. ‘Kazanskaya 560’. The values of protein content in grain were medium or high. The following accessions had high and stable levels of protein content in grain (15.1–16.1%): ‘TAW 42971/80’ (k-58363, Germany); ‘Lutescens 471 N8’ (Kazakhstan); ‘Rita’ (k-58057), ‘Scotty’ (k-59322) and ‘Nelson’ (all from the U.S.); ‘Moskovskaya 39’ (k-65160, Russia); ‘Bilotserkivchanka’ (k-64330) and ‘Barkan’ (k-64495) (both from Ukraine). Flour swelling power in acetic acid did not fall below 50 ml, attesting to the formation of high-quality grain. This was also confirmed by the protein quality index determined by the S : P ratio, which ranged from 3.6 to 4.7. Sources with high-quality protein were selected from the tested accessions for use in breeding: ‘CDC Clair’ (k-64168, Canada), ‘Lutescens 471 Н8’ (Kazakhstan), ‘Moskovskaya 39’ (Russia), ‘Barkan’ (Ukraine), and ‘Favorytka’ (k-64337, Ukraine).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.025 | 0.012 |
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".