Genetic analysis of walnut cultivars from southwest China: Implications for germplasm improvement
Bibliographic record
Abstract
Walnuts are highly valued for their rich nutritional profile and wide medicinal applications. This demand has led to the intensification of breeding activities in major walnut production areas such as southwest China, in order to develop more superior cultivars. With the increasing number of cultivars, accurate identification becomes fundamental to selecting the right cultivar for grafting, industrial processing or development of new cultivars. To ensure proper identification of cultivars and understand the genetic structure of wild and cultivated material, we genotyped 362 cultivated and wild individuals of walnut trees from southwest China (with two additional populations from Xinjiang, plus three cultivars from Canada, France and Belgium) using 36 polymorphic microsatellite loci. We found relatively low indices of genetic diversity (HO = 0.570, HE = 0.404, NA = 2.345) as well as a high level of clonality (>85% of cultivars), indicating reliance on genetically narrow sources of parental material for breeding. Our STRUCTURE and PCoA analyses generally delineated the two species, though considerable levels of introgression were also evident. More significantly, we detected a distinct genetic group of cultivated Juglans sigillata, which mainly comprised individuals of the popular ‘Yangbidapao’ landrace. Finally, a core set of 18 SSR loci was selected, which was capable of identifying 32 cultivars. In a nutshell, our results call for more utilization of genetically disparate material, including wild walnut trees, as parental sources to breed for more cultivars. The data reported herein will significantly contribute towards the genetic improvement and conservation of the walnut germplasm in southwest China.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| 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".