Simple Genetic Distance-Optimized Field Deployments for Clonal Seed Orchards Based on Microsatellite Markers: As a Case of Chinese Pine Seed Orchard
Bibliographic record
Abstract
Chinese pine seed orchards are in a period of transition from first-generation to advanced-generations. How to effectively select populations for second-generation seed orchards and significantly increase genetic gain through rational deployment have become major issues. In this study, we examined open- and control-pollinated progeny of the first-generation Chinese pine seed orchards in Zhengning (Gansu Province, China) and Xixian (Shanxi Province, China) to address issues related to phenotypic selection for high volume growth, genetic diversity analysis and genetic distance-based phylogenetic analysis of the selections by simple sequence repeats (SSRs), and phylogenetic relationship-based field deployment for advanced-generation orchards. In total, 40, 28, 20, and 13 superior individuals were selected from the large-scale no-pedigree open-pollinated progeny of Zhengning (ZN-NP), open-pollinated families of Zhengning (ZN-OP), open-pollinated families of Xixian (XX-OP), and control-pollinated families of Xixian, with mean volume dominance ratios of 0.83, 0.15, 0.25, and 0.20, respectively. Phylogenetic relationship analysis of the ZN-NP and XX-OP populations showed that the 40 superior individuals in the ZN-NP selected population belonged to 23 families and could be further divided into five phylogenetic groups, and that families in the same group were closely related. Similarly, 20 families in the XX-OP population were related to varying degrees. Based on these results, we found that second-generation Chinese pine seed orchards in Zhengning and Xixian should adopt a grouped, unbalanced, complete, fixed block design and an unbalanced, incomplete, fixed block design, respectively. This study will provide practical references for applying molecular markers to establishing advanced-generation seed orchards.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".