The effect of <i>VRN1</i> genes on important agronomic traits in high‐yielding <scp>C</scp>anadian soft white spring wheat
Bibliographic record
Abstract
Abstract For reproductive success, flowering time must synchronize with favourable environmental conditions. Vernalization genes play a major role in accelerating or delaying the time to flowering. We studied how different vernalization ( VRN 1 ) gene combinations alter days to flowering and maturity and consequently the effect on grain yield and other agronomic traits. The study focussed on the effect of the VRN 1 gene series ( V rn‐ A 1, V rn‐ B 1 and V rn‐ D 1 ) and their combinations. The V rn gene group V rn‐ A 1a, V rn‐ B 1, vrn‐ D 1 was the earliest to flower and mature, while V rn‐ A 1b, V rn‐B1, vrn‐ D 1 was the latest to flower. Spring wheat lines with vrn‐ A 1, V rn‐ B 1, V rn‐ D 1 were the highest yielding and matured at a similar time as those having vernalization genes V rn‐ A 1a, V rn‐ B 1 and V rn‐ D 1 . The findings of this study suggest that the presence of V rn‐ D 1 has a direct or indirect role in producing higher grain yield. We therefore suggest the introduction of V rn‐ D 1 allele into higher‐yielding classes within C anadian spring wheat germplasm.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 teacher head, 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".