Genetic diversity of different apricot geographical groups determined by SSR markers
Bibliographic record
Abstract
Forty apricot cultivars with different geographic origins belonging to the germplasm collections of St. Istvan University (Budapest, Hungary) and the Instituto Valenciano de Investigaciones Agrarias (IVIA) (Valencia, Spain) were studied by means of SSR markers. The aim of the study was to determine the genetic relationships among genotypes from different eco-geographical groups. Sixteen primer pairs flanking microsatellite sequences in the peach genome were assayed. Eleven of them were polymorphic in the set of cultivars studied and allowed every genotype to be unambiguously distinguished. Genetic diversity in the population studied was analyzed using several variability parameters. A total of 34 alleles were detected with a mean value of 3.1 alleles/locus. The expected heterozygosity mean was 0.46 and the observed heterozygosity was 32% on an average leading to a high value of the Wright's fixation index (0.32). Additionally, UPGMA cluster analysis based on Nei's genetic distance grouped genotypes according to their geographic origins and pedigrees. SSR markers have proved to be an efficient tool for fingerprinting cultivars and conducting genetic-diversity studies in apricot.
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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".