IDENTIFICATION OF HIGH MOLECULAR GLUTENINS IN SPRING SOFT WHEAT VARIETIES OF CANADIAN SELECTION
Bibliographic record
Abstract
Мягкая пшеница (Triticum aestivum L.) – важнейшая сельскохозяйственная культура, являющаяся основой хлебопекарного производства. В настоящее время увеличился спрос на высококачественные продукты питания, в том числе хлебобулочные изделия, однако качество зерна и выпекаемого хлеба, не смотря на рост производства, неуклонно снижается (Карпушин, 2017). Агротехническими приёмами сложно, дорого, а, зачастую, вообще невозможно повысить качество зерна, следовательно, в процессе селекции необходимо на генетическом уровне обеспечивать высокий технологический и хлебопекарный потенциал сортов мягкой пшеницы. Soft wheat (Triticum aestivum L.) is the most important agricultural crop, which is the basis of bakery production. Currently, the demand for high-quality food products has increased, including bakery products, but the quality of grain and baked bread, despite the growth in production, is steadily declining (Karpushin, 2017). It is difficult, expensive, and often impossible to improve the quality of grain by agricultural techniques, therefore, in the breeding process, it is necessary to ensure the high technological and baking potential of soft wheat varieties at the genetic level.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".