Microsatellite-based genetic relationships in the genus<i>Camellia</i>: potential for improving cultivars
Bibliographic record
Abstract
There is a lack of published microsatellite data that characterizes Camellia spp. To address this, an initial study of sequence tagged microsatellite site (STMS) variation was undertaken with 132 accessions of Camellia spp., which included 24 accessions representing 22 different species or varieties as well as 63 cultivars of C. japonica, 33 cultivars of C. sasanqua, 7 cultivars of C. x vernalis, 3 cultivars of C. x hiemalis, and 2 cultivars of C. hybrida. The four primer sets used (MSCJAF37, MSCJAH46, MSCJAF25, and MSCJAH38) successfully amplified polymorphic alleles in all the species analysed, showing cross-transferability. Overall, 96 alleles were scored. MSCJAH38 primers produced the highest number of bands (30), while MSCJAH46 primers yielded the lowest number (15). The genetic distance between pairs of accessions was estimated on the basis of the Nei coefficient and a principal coordinate analysis was performed. The plot revealed a main differentiation between the C. japonica cultivars and the winter camellias. The distribution of the genetic variation, attributed by AMOVA, particularly highlighted genetic overlap among C. sasanqua cultivars and the cultivars belonging to C. x vernalis, C. x hiemalis, and C. hybrida. In conclusion, this study demonstrated that STMS markers offer a suitable method for detection of genetic variability and molecular study of camellia genotypes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".