Genetic and chemical diversity analyses in tale grapes (Vitis vinifera L.)
Bibliographic record
Abstract
Vitis vinifera L. is one of the economically important plant crops worldwide which is a valuable food source for humans. This precious plant species has several local varieties and accessions which are continuously under selection and cultivation. Due to these human activities, the grape faces genetic homogeneity and erosion. Therefore, it is important to investigate available genetic diversity in grape plants all over the world. We aimed to study the genetic structure and diversity as well as chemical differences of seven grape cultivars in the country. We used SSR, and SRAP molecular markers for genetic diversity analyses, as well as biochemical traits. Both molecular markers showed a medium to moderate genetic variability in the studied grape cultivars (about 20% genetic polymorphism). Similarly, both molecular markers differentiated the studied cultivars into two genetic groups. AMOVA indicated significant genetic difference in these cultivars. ANOVA analysis of flavonols (quercetin, myricetin, kaempferol, and rutin) contents of seeds extract by HPLC indicated the significant difference (P <0.01) among grape cultivars. PCA biplot of cultivars based on chemical features separated these cultivars into two major groups according to their flavone and flavonoid contents. Pairwise Mantel tests performed between molecular and chemical data showed a significant association between SSR and SRAP data, but no significant association was obtained between either SSR or SRAP data with chemical features in grape cultivars studied. A heat-map constructed based on combined molecular and chemical data revealed that some of the studied grape cultivars are distinct in their genetic and chemical features.
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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 | 0.002 | 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".