Influence of low‐molecular‐weight glutenin subunit genes at <i>Glu‐<scp>A3</scp></i> locus on wheat sodium dodecyl sulfate sedimentation volume and solvent retention capacity value
Bibliographic record
Abstract
BACKGROUND: To understand the effect of low-molecular-weight (LMW) glutenin alleles at the Glu-A3 locus on sodium dodecyl sulfate (SDS) sedimentation volume and solvent retention capacity (SRC) values, 244 accessions of Chinese wheat (Triticum aestivum L.) mini core collections were investigated. In this study the significant differences in wholemeal flour SDS sedimentation volume and SRC values associated with specific glutenin alleles at the Glu-A3 locus were explained. RESULTS: Seven glutenin alleles at the Glu-A3 locus were confirmed by locus-specific polymerase chain reaction (PCR). SDS sedimentation volume and lactic acid SRC value were significantly affected by alleles Glu-A3b and Glu-A3g. Based on total average values, 28 varieties carrying Glu-A3b had significantly higher means of SDS sedimentation volume and lactic acid SRC value, whereas 19 varieties carrying Glu-A3g had significantly lower means. Alleles Glu-A3d and Glu-A3f significantly increased only SDS sedimentation volume and sucrose SRC value respectively. Correlation analysis showed that SDS sedimentation volume was uncorrelated with lactic acid SRC and sucrose SRC values. CONCLUSION: The Glu-A3 LMW glutenin subunit could predict 12.8% of the variance in SDS sedimentation volume, 4.7% in lactic acid SRC and 6.4% in sucrose SRC.
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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 | 0.001 |
| 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".