Genetic diversity and population genetic structure in <i>Kadsura coccinea</i> (Schisandraceae), an evergreen woody vine from Hunan, China
Bibliographic record
Abstract
Kadsura coccinea is an evergreen woody vine species indigenous to Southeast Asia and south China with great promise in medicine, food, and ornamental gardening. Its natural resources have decreased dramatically due to irrational fruits and roots harvesting in two decades recently, which may result in heavy erosion of its genetic resources, especially in Hunan Province, China. Here 406 individuals of K. coccinea were collected from 11 natural populations in Hunan Province, and were analyzed for genetic variation using 17 polymorphic microsatellite markers. A total of 163 alleles were amplified with a mean of 9.588 alleles per locus. The mean effective number of alleles per locus, allele richness and the expected heterozygosity of these populations were 2.910, 4.171 and 0.558 on average, respectively, indicating a bit low level of genetic diversity. The among-population differentiation (FST) accounted for 13.63% of the total genetic variation, and analysis of molecular variance also showed 13.38% of genetic variation existed among populations, representing a high genetic differentiation and a bit limited gene flow (Nm = 1.813) among these populations. The results of Bayesian clustering analysis and neighbor-joining clustering analysis showed that populations GD and YZ were specific, and CB and HT were assigned into a cluster. The mantel test demonstrated that the genetic distances revealed by pairwise FST between populations were remarkably correlated to geographic distances. It is the first report on genetic diversity of species in genus Kadsura using molecular markers, and the findings provide fundamental base for genetic resources conservation and breeding of this species.
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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) | 0.000 | 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 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".