High level of genetic diversity among spelt germplasm revealed by microsatellite markers
Bibliographic record
Abstract
The genetic diversity of spelt (Triticum aestivum (L.) Thell. subsp. spelta (L.) Thell.) cultivated presently is very narrow. Although the germplasm collections of spelt are extensive, the related genetic knowledge is often lacking and makes their use for genetic improvement difficult. The genetic diversity and structure of the spelt gene pool held in gene banks was determined using 19 simple sequence repeat (SSR) markers applied to 170 spelt accessions collected from 27 countries and 4 continents. The genetic distances (1 - proportion of shared alleles) were calculated and an unweighted pair-group method with arithmetic averaging (UPGMA)-based dendrogram was generated. The genetic diversity was high: 259 alleles were found and the mean interaccession genetic distance was 0.782 +/- 0.141. The dendrogram demonstrated the much higher genetic diversity of spelt held in germplasm collections than in the currently used genotypes. Accessions with the same geographical origin often tended to cluster together. Those from the Middle East were isolated first. All but one of the Spanish accessions were found in a unique subcluster. Most accessions from eastern Europe clustered together, while those from northwestern Europe were divided into two subclusters. The accessions from Africa and North America were not separated from the European ones. This analysis demonstrates the extent of genetic diversity of spelts held in germplasm collections and should help to widen the genetic basis of cultivated spelt in future breeding programs.
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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 | 0.001 |
| 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".