Copy number variation at <i>Vrn-A1</i> and haplotype diversity at <i>Fr-A2</i> are major determinants of winter survival of winter wheat (<i>Triticum aestivum</i> L.) in Eastern Canada
Bibliographic record
Abstract
Winter survival is an essential trait for winter wheat ( Triticum aestivum L.) cultivars grown in high latitude regions such as Eastern Canada. Indoor studies have identified that copy number variation of genes influencing freezing is an essential component. Although Canadian winter wheat is predominantly grown in Eastern Canada, the extent to which allele variation in freezing tolerance genes affects winter survival in this region remains unknown, as there are presently no studies characterizing such variation in Canadian winter wheat germplasm. In this study, we characterized a panel 415 Canadian winter wheat cultivars for haplotype diversity of the Frost Resistance-2 ( Fr-A2) locus and copy number variation of Vernalization-A1 ( Vrn-A1) and C- repeat binding factors- A14 ( CBF-A14). Additionally, this study evaluates each gene’s effect on winter survival across two locations and 2 years. We found that a combination of Vrn-A1 copy number and Fr-A2 haplotype accounted for 67.38% of the genotypic variance. Most of the cultivars tested (77.3%) carry the allele combination of three copies of Vrn-A1 and the Fr-A2-T haplotype, which was associated with the best winter survival. Interestingly, copy number of Vrn-A1 did not significantly affect heading time, therefore, selecting for higher copy number of Vrn-A1 would not affect maturity.
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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 | 0.001 | 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".