Distribution of β‐Glucan in the Grain of Hull‐less Barley
Bibliographic record
Abstract
ABSTRACT Nine hull‐less barley (HB) containing waxy (0–7% amylose), normal (≈25% amylose), or high amylose (≈42% amylose) starch with normal or fractured granule make‐up and 4–9% (1→3)(1→4)‐β‐d‐glucans (β‐glucan) were pearled to remove 70% of the original grain weight in 10% intervals. The pearled fractions were analyzed for β‐glucan distribution within HB grain. Protein content of the pearled fractions indicated that the three outermost fractions contained pericarp and testa, aleurone, and subaleurone tissues, respectively. For all HB, β‐glucan and acid‐extract viscosity were very low in the outermost 20% of the kernel. For low β‐glucan HB, β‐glucan content was the greatest in the subaleurone region and declined slightly toward inner layers. For high β‐glucan HB, however, more than 80% of grain β‐glucan was distributed more evenly throughout the endosperm. Acid extract viscosity was significantly (P < 0.01) correlated with total (r = 0.75) and soluble (r = 0.87) β‐glucan content throughout the kernel of all HB. Growing conditions, location and year, had significant effects on the concentration of protein, starch and β‐glucan. However, protein, starch, and β‐glucan distribution patterns were not affected by growing conditions. The difference in β‐glucan distribution between low and high β‐glucan HB may explain the difference in milling performance of HB with low or high β‐glucan.
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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 | 0.000 | 0.000 |
| 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".