Технологічні та хлібопекарські властивості зразків пшениці м’якої ярої залежно від походження
Bibliographic record
Abstract
Aim. Identify priorities for the introduction of common spring wheat with high grain quality in connection with the geographical origin of the samples. Select sources of valuable traits. Material and methods. The article summarized the results of trial during 1997-2010's technological and baking properties of 253 samples of spring bread wheat from 15 countries. Results. The regularities of grain quality characteristics appearance depending on geographical origin of samples were evaluated. Varieties and lines from the steppe zone of Russia and Kazakhstan had the best technological and baking properties among of bread spring wheat samples, but they were characterized by a lower content of protein and gluten in the flour compared with varieties from the north of Ukraine, Germany, Belarus, Canada and USA. Geography origin varieties and lines with high values of test weight, grain vitreousness, protein and gluten content in the flour and samples with high technological and baking qualities are different. There was no negative correlation between grain yield and flour strength, bread volume and common baking score. Sources of complex important technological, baking traits and high yield were identified among bread spring wheat collection. Clustering regions of origin by dedicated quality factors allowed us to determine priorities for the introduction of samples with high grain quality characteristics, which included Forest-steppe and Steppe of Russia and Kazakhstan. Conclusions. As a result of study the priorities for introduction of common spring wheat with high grain quality were identified, the sources of valuable traits were selected and the trait collection of spring wheat according to technological and baking qualities were formed.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".