Selection of a seed orchard of Eucalyptus dunnii based on genetic diversity criteria calculated using molecular markers
Bibliographic record
Abstract
A Eucalyptus dunnii Maiden breeding population of 46 accessions originated in Australia and selected for fitness to subtropical and cold environments was screened by Amplified Fragment Length Polymorphism (AFLP) and microsatellite markers to obtain quantitative estimates of genetic diversity. A randomly chosen group of AFLP primers generated 205 AFLP bands that were used to fingerprint the genotypes and to evaluate genetic relationships among accessions. Sixty-eight percent (140) of the bands were polymorphic markers. The mean diversity index (DI) was 0.33 and about 52% of the loci had values greater than 0.4. Cluster analysis derived from similarity indices (SI) revealed no particular grouping among accessions suggesting the absence of closely related genotypes, except for five pairs of genotypes. Bootstrap analysis results confirmed the suitability of AFLP to describe genetic relationships in this breeding population. In addition, four highly informative microsatellites were used to construct an identification matrix that discriminated nearly all of the genotypes. Mean values for the number of alleles per locus, DI and SI among accessions were 13, 0.78 and 0.19, respectively, indicating that the breeding population has high genetic diversity. However, several genotypes showed the presence of single microsatellite bands suggesting a putatively important degree of homozygosity. Molecular data were used to design a clonal seed orchard. To achieve this aim, the nine most divergent pairs of genotypes were chosen, thereby retaining 95.2% of the total number of alleles from the 140 polymorphic AFLP loci and the four microsatellite loci analyzed. Mean DI and SI for AFLP and microsatellites showed no significant differences between the original breeding population and the selected seed orchard, confirming that a seed orchard can be designed with a limited number of individuals, which allows similar accessions to be discarded and avoids inbreeding.
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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 | 0.000 |
| 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) | 0.000 | 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".