Discovering Genes that Enhance Yield in Drought Conditions within Turkish Winter Wheat
Bibliographic record
Abstract
On April 10, 2024, a collaborative research result by the International Maize and Wheat Improvement Center in Mexico, Syngenta's Jealott's Hill International Research Center, and Kazakhstan's Scientific Production Grain Center was published in the journal Scientific Reports. The paper, authored by D. Sehgal as the first author and A. Morgounov as the corresponding author, is titled "Genomic wide association study and selective sweep analysis identify genes associated with improved yield under drought in Turkish winter wheat germplasm." This research was jointly funded by the FAO's International Treaty on Plant Genetic Resources for Food and Agriculture (W2B-PR-41-TURKEY) and the BMGF/FCDO project on Accelerating Genetic Gains in Maize and Wheat for Improved Livelihoods (AGG) (INV-003439). The study employed genome-wide association studies (GWAS) and selective sweep analysis to explore genes and genomic regions related to drought resistance and increased yield within Turkish winter wheat germplasm. The research involved genotyping 84 local Turkish winter wheat varieties and 73 modern varieties using a 25K wheat SNP array and phenotyped agronomic traits in 2018 and 2019. The year 2018 was considered a drought environment due to extremely low rainfall, while 2019 was deemed a favorable environment. The results indicated several genomic regions associated with yield and yield-related traits.
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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".