Monitoring genetic diversity across <i>Pinus tabuliformis</i> seed orchard generations using SSR markers
Bibliographic record
Abstract
The maintenance of genetic diversity across seed orchard generations is an important management objective. Here, we used Pinus tabuliformis Carrière as a model to explore the extent of genetic diversity across the species’ breeding activities through their corresponding seed orchard generations. We utilized a large number of simple sequence repeat (SSR) markers selected from P. tabuliformis transcriptomic data, and then assessed the effect of marker number on genetic diversity and genetic relationships of individuals across orchard generations. In total, we designed 125 SSR markers, of which 39 were polymorphic and used in the present study. The genetic diversity and genetic distance parameters tended to increase with an increase in loci number and a stable trend was reached at 24 SSRs. The selected optimal 24 SSR markers were further used to assess the genetic diversity across seed orchard generations, and a decreasing trend was detected with the advancement of orchard’s generations. Genetic distance analysis indicated that individuals in the 2nd generation orchard were more closely related compared with those of the 1st generation and the 1.5 generation. This study provided valuable information on the effect of selection and breeding on genetic diversity and highlighted its role for effective seed orchard management.
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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 | 0.000 | 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".