Cultivar and Environmental Variation of β-glucan Content in Chinese Barleys
Bibliographic record
Abstract
β-glucan is a polysaccharide compound closely related to the quality of barley used as malting,feed and food. Low β-glucan content is expected for brewing and feed barley, while high β-glucan content is desirable for food barley. The β-glucan content of barley genotypes collected from various areas of China as well as from Canada and Australia were assayed. Meanwhile a multi-locations trial was conducted to determineβ-glucan content of 10 barley cultivars in 8 locations for two successive planting years. The results showed that barley genotypes from Tibet and Xinjiang had higher β-glucan content and the genotypes with higher than 8%of β-glucan content were detected in Tibet barleys, being valuable for use in the development of healthy food.Barley cultivars being planted now in winter-sowing areas of China had basically the same β-glucan content as those from Canada and Australia. Barley seeds produced in Hangzhou had lower β-glucan content than seeds from the original areas. There was a highly significant difference in β-glucan content among 10 barleys, 8locations and between years. On an average of two years, Xiumei 3 and Kongpei 1 had the highest and lowestβ-glucan content, respectively, and Taian and Hangzhou produced the highest and lowest β-glucan content barley seeds, respectively. Analysis of AMMI model showed that interaction effect between cultivar and environment was highly significant in both experimental years, and was dependent on cuitivar, suggesting that it is important to plant the suitable cultivars in a particular area in order to obtain barley seeds with reasonableβ-glucan content.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".