Alveograph and Mixolab parameters associated with <i>Puroindoline‐D1</i> genes in Chinese winter wheats
Bibliographic record
Abstract
BACKGROUND: Grain texture is one of the most important characteristics of bread wheat and has a significant influence on end-use qualities. RESULTS: Forty-three Chinese cultivars were tested under three environments and used to characterise kernel hardness, Puroindoline-D1 alleles and Alveograph and Mixolab parameters. The results indicated that SKCS hardness was positively correlated with Alveograph tenacity and P/L and Mixolab protein weakening (C2) and water absorption and negatively correlated with Mixolab starch gelatinisation (C3), amylasic activity (C4) and starch gelling (C5). Variance analysis showed that Puroindoline-D1 had a significant impact on SKCS hardness and most Alveograph and Mixolab parameters. Furthermore, among three Puroindoline-D1 genotypes, PINA-null/Pinb-D1a possessed the highest SKCS hardness, Alveograph tenacity and W and Mixolab stability and water absorption but the lowest Alveograph extensibility and G and Mixolab C3, C4 and C5. Pina-D1a/Pinb-D1a had the lowest SKCS hardness, Alveograph tenacity and W and Mixolab C2, water absorption and stability but the highest Alveograph extensibility and G and Mixolab C3, C4 and C5. Pina-D1a/Pinb-D1b possessed the lowest Mixolab C2 - C1, C3 - C2, C4 - C3 and C5 - C4. CONCLUSION: Pina-D1a/Pinb-D1a was softer and had lower tenacity and water absorption. PINA-null/Pinb-D1a was harder and had higher tenacity and water absorption. Pina-D1a/Pinb-D1b had lower difference values among Mixolab parameters.
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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".