Comparison of genetic diversity structure analyses of SSR molecular marker data within apple (<i>Malus</i>×<i>domestica</i>) genetic resources
Bibliographic record
Abstract
The aim of this study was to compare traditional hierarchical clustering techniques and principal coordinate analysis (PCoA) with the model-based Bayesian cluster analyses in relation to subpopulation differentiation based on breeding history and geographical origin of apple (Malus×domestica Borkh.) cultivars and landraces. We presented the use of a set of 10 microsatellite (SSR) loci for genetic diversity structure analyses of 273 apple accessions from national genetic resources. These SSR loci yielded a total of 113 polymorphic SSR alleles, with 5-18 alleles per locus. SSR molecular data were successfully used in binary and allelic input format for all genetic diversity analyses, but allelic molecular data did not reveal reliable results with the NTSYS-pc and BAPS softwares. A traditional cluster analysis still provided an easy and effective way for determining genetic diversity structure in the apple germplasm collection. A model-based Bayesian analysis also provided the clustering results in accordance to traditional cluster analysis, but the analyses were distorted by the presence of a dominant group of apple genetic resources owing to the narrow origin of the apple genome. PCoA confirmed that there were no noticeable differences in genetic diversity structure of apple genetic resources during the breeding history. The results of our analyses are useful in the context of enhancing apple collection management, sampling of core collections, and improving breeding processes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 teacher head, 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".