Genetic Diversity Analysis of Youxi Bitter Tea Resources Based on ISSR Molecular Markers
Bibliographic record
Abstract
In this study, we analyzed the genetic diversity by using ISSR markers in four populations with 37 DNA samples of Youxi Kucha. In total, 66 alleles were amplified using 8 ISSR primers. A total of 66 bands were detected, of which 63 bands were polymorphic with a polymorphic proportion of 95.24 %. At the population level, the average PPL of the four populations of Youxi Kucha is 76.19 %, and the average values of Nei's gene diversity index ( H ) and Shannon diversity index ( I ) are 0.204 5 and 0.367 7 respectively. At the species levelm the H and I are 0.305 1 and 0.465 0 respectively respectively. Youxi Kucha maintained a relatively high genetic variability at the population level and species level bothly. The genetic differentiation within the population was significantly higher than that between populations ， but the genetic differentiation between the populations has also reached higher level ( Φ st = 0.17> 0.15), the differentiation is extremely significant (P <0.01). A mantel test indicated there was no significant relationship between genetic distance and geographic distance among the populations studied. Correlation analysis shows that the diversity index has little relationship with altitude. This study carried out the molecular identification of the germplasm resources of Youxi bitter tea accurately, which can provide a theoretical basis for the protection of Youxi bitter tea and the breeding of improved tea varieties.
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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) | 0.001 | 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".