Core construction establishment for Ruchengbaimao tea based on SSR markers
Bibliographic record
Abstract
Germplasm collection and conservation requires many efforts and resources. Core collection construction could both conserve genetic diversity and improve conservation efficiency. This study investigated the genetic diversity of tea plants [ Camellia sinensis (L.) O. Kuntze] collected from four regions of Rucheng using 14 simple sequence repeat (SSR) markers and constructed a core collection set. A phylogenetic tree and population structure were conducted. A core set of 28 tea plants, approximately 10% of 279 tea plant individuals, was constructed to capture the samples' maximum genetic diversity. Compared to the original collection, the retention rates of N a , I , H o , H e , MAF, and PIC in the core collection were 106.4%, 118.8%, 103.1%, 112.2%, 68.1%, and 113.1%, respectively. The significance of core germplasm lies in identifying and conserving a set of representatives, diverse, and genetically advantageous genetic resources to support subsequent genetic improvement and breeding efforts. It could serve as a foundation for conserving valuable genetic materials and identifying loci associated with important horticultural traits, thereby empowering the development of new tea cultivars with enhanced efficiency. Furthermore, this approach contributes to optimizing breeding strategies, accelerating the selection process, and ensuring the sustainability of tea plant genetic resources for future generations.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".