Alternative strategies for partner notification: a missing piece of the puzzle
Bibliographic record
Abstract
<h3>Abstract</h3> Wheat (<i>Triticum aestivum</i> L.) is a major staple crop in East Africa (EA) providing 9% and 10% of daily calories and protein intake, respectively. However, EA countries depend on import to meet 55% of their domestic wheat supplies due to increasing demands and low domestic yields. To determine the beneficial gene pool currently exploited for wheat improvement in EA, we examined the allelic diversity of 42 genes of breeding importance in a collection of 239 wheat cultivars and breeding lines from Kenya and Ethiopia using KASP markers. The assayed genes have been shown to control variations in plant height, thousand kernel weight (TKW), grain protein content, pre-harvest sprouting (PHS), disease resistance and flowering time. We observed the beneficial alleles of some major genes including <i>Rht-D1, Gpc-B1, Yr5, Yr15, Sr26</i>, and <i>Fhb1</i> to be missing or present at low frequencies in this population. Furthermore, we validated the effects of the major <i>Rht-1</i> alleles and <i>TaCKX-6A</i> in controlling variation in plant height and thousand kernel weight, respectively, under EA conditions. Our results uncover hitherto unexploited allelic diversity that can be used to improve the genetic potential of EA wheat germplasm. This will inform strategies to rapidly mobilize these beneficial alleles for wheat improvement in EA.
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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 | 0.000 | 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".