Genetic diversity and relationships among Dutch elm disease tolerant<i>Ulmus pumila</i>L. accessions from China
Bibliographic record
Abstract
Elm breeding programs worldwide have relied heavily on Asian elm germplasm, particularly Ulmus pumila, for the breeding of Dutch elm disease tolerant cultivars. However, the extent and patterning of genetic variation in Asian elm species is unknown. Therefore, the objective of this research was to determine the extent of genetic diversity among 53 U. pumila accessions collected throughout the People's Republic of China. Using 23 microsatellite loci recently developed in the genus Ulmus, a total of 94 alleles were identified in 15 polymorphic and 4 monomorphic loci. The average number of alleles per locus was 4.9, with a range of 1-11 alleles. Gene diversity estimates per locus ranged from 0.08 to 0.87, and the non-exclusion probability for the 15 polymorphic loci combined was 0.7 x 10(-9). Nineteen region-specific alleles were identified, and regional gene diversity estimates were moderately high (0.48-0.57). The genetic relationships among accessions and regions were estimated by UPGMA and principal coordinate analysis. Both techniques discriminated all accessions and regions. Two microsatellite markers (UR175 + UR123 or Ulm-3) were sufficient to discriminate up to 99.7% of the accessions studied. This research provides useful information for DNA-based fingerprinting, breeding, ecological studies, and diversity assessment of elm germplasm.
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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) | 0.000 | 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".