Starch Granule Size Distribution from Seven Wheat Cultivars Under Different Water Regimes
Bibliographic record
Abstract
ABSTRACT Seven wheat cultivars with different starch contents were used as materials to investigate the distribution of grain starch granule size under irrigated and rainfed conditions. In mature grains, the diameter of starch granules was 0.37–52.6 μm, and the percent volume distribution showed a two‐peak curve with the mean particle diameter of 5 (B‐type) and 25 μm (A‐type) at each peak. The volume percentages of A‐ and B‐types were 52.7–65.5% and 34.5–47.3%, respectively. A two‐peak curve is also shown in percent surface area distribution of starch granules, but only one peak in percent number. Both irrigated and rainfed conditions had a significant effect on the starch granule size distribution of the seven cultivars. As compared with irrigated treatment, rainfed treatment affected the distribution of starch granules in grains of all cultivars through increasing the volume percentage and surface area percentage of 2–9.8 and <9.8 μm starch granules and decreasing those of >9.8 and >18.8 μm starch granules. The soil water deficit also decreased the contents of amylose and starch in grains and increased protein content, indicating that different water regimes had an evident effect on grain quality. According to correlation coefficients (r), the contents of amylose, starch, and protein in grains was significantly correlated with the volume percentage of starch granules with different diameter ranges.
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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) | 0.000 | 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".