Development of EST-SSR Markers and their Use in Assessing Genetic Diversity in Chinese Fir Infusion Populations
Bibliographic record
Abstract
Abstract In advanced-generation tree breeding program, infusion populations are often used as an effective method to expand and maintain genetic diversity. To analyze the genetic diversity and population structure of six Chinese fir ( Cunninghamia lanceolata ) geographical populations, a total of 20 expressed sequence tag-derived simple sequence repeat markers pairs (EST-SSR) were developed and applied for genetic diversity analysis. The evaluated populations exhibited moderate genetic diversity with the following parameters: number of alleles ( N a : 4.850), effective number of alleles ( N e : 2.920), information index ( I : 0.958), observed heterozygosity ( H o : 0.319), expected heterozygosity ( H e : 0.481), unbiased heterozygosity ( uH e : 0.496), and fixation index ( F : 0.321). The analysis of molecular variance (AMOVA) suggested that only 9.42 % of genetic variation existed among populations, whereas the majority (90.58 %) resided within populations. Cluster analysis showed one population (Sichuan Dechang) as a separate taxon, likely due to its geographical isolation. The present study demonstrated the effectiveness of the developed EST - SSR in analyzing genetic diversity for population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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) | 0.000 | 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".