Genetic Diversity of Coconut Cultivars in China by Microsatellite (SSR) Markers
Bibliographic record
Abstract
Assessment of genetic diversity is an essential component in germplasm characterization and utilization. In this study, we determined genetic diversity of 10 coconut ( Cocos nucifera L.) accessions from six locations in Hainan province, China by using microsatellite markers. From the used 26 simple sequence repeat (SSR) markers, we detected a total of 188 alleles with an average of 7.23 alleles per locus and an average polymorphism information content of 0.575. Expected heterozygosity ( He ) of Haikou green Tall (HK-GT) was significantly higher than that of other Tall types, while the lowest heterozygosity was observed in Sanjiang green Tall (SJ-GT). At the genetic differentiation index ( F ST ) of 0.078, they showed a low level of population differentiation. In addition to diversity parameters, Bayesian assignment tests and cluster analysis were used to determine population structure. Our study provided a better understanding of individual identities, genealogical relationships and geographical origin of coconut germplasm, and it could contribute to more efficient conservation and utilization of this germplasm.
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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 | 0.002 |
| 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 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".