High‐molecular‐weight glutenin subunit compositions in current Chinese commercial wheat cultivars and the implication on Chinese wheat breeding for quality
Bibliographic record
Abstract
Abstract Background and objectives Genetic diversity of high‐molecular‐weight (HMW) glutenin subunits is often used for wheat improvement because of its correlation with end‐use quality of wheat. The objectives were (a) to characterize and analyze the HMW‐GS composition of common wheat, (b) determine the diversity of high‐molecular‐weight glutenin subunits, and (c) elucidate the distribution characteristics of wheat good‐quality subunit and quality classification. Findings The results revealed a total of 15 different alleles (3 at Glu‐A1, 8 at Glu‐B1, and 4 at Glu‐D1) and 35 allele combinations. Glu‐A1c (57.92%), Glu‐A1a (62.94%), Glu‐A1c (55.86%) appeared to be the most frequent alleles at Glu‐A1 in Hebei, Henan, and Sichuan provinces, respectively. The Glu‐B1c was the most common allele at the Glu‐B1 locus in all three provinces, with frequencies of 48.75%, 55.24%, and 29.66%, respectively. Glu‐D1a (66.25%), Glu‐D1d (53.85%), Glu‐D1a (55.17%) at the Glu‐D1 locus appeared to be the common alleles in Hebei, Henan, and Sichuan provinces, respectively. Cluster analysis based on allelic similarity of Glu‐1 loci classified the 528 wheat cultivars into two major categories. Conclusions These results indicated that the HMW glutenin allele compositions are not optimal for the current varieties in these provinces. A high level of quality improvement may be implemented by increasing the frequencies of Glu‐A1a (1) or Glu‐A1b (2*), Glu‐B1i (17 + 18), Glu‐D1d (5 + 10). Significance and novelty Our results revealed clear directions for wheat quality improvement in these provinces.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".