Specific genetic markers for wheat, spelt, and four wild relatives: comparison of isozymes, RAPDs, and wheat microsatellites
Bibliographic record
Abstract
Three types of markers-isozymes, RAPDs (random amplified polymorphic DNAs), and wheat microsatellites- were tested on wheat, spelt, and four wild wheat relatives (Aegilops cylindrica, Elymus caninus, Hordeum marinum, and Agropyron junceum). The aim was to evaluate their capability to provide specific markers for differentiation of the cultivated and wild species. The markers were set up for subsequent detection of hybrids and introgression of wheat DNA into wild relatives. All markers allowed differentiation of the cultivated from the wild species. Wheat microsatellites were not amplified in all the wild relatives, whereas RAPDs and isozymes exhibited polymorphism for all species. The dendrograms obtained with RAPD and isozyme data separated Swiss wheat cultivars from those collected in Austria and England, while no difference was found between Swiss spelt and wheat. RAPD data provided a weak discrimination between English and Austrian E. caninus. The microsatellite-based dendrogram discriminated populations of Ae. cylindrica, but no clear separation of H. marinum from E. caninus was revealed. The similarity matrices based on the three different sets of data were strongly correlated. The highest value was recorded between the matrices based on RAPDs and isozymes (Mantel's test, r = 0.93). Correlations between the similarity matrix based on microsatellites and matrices based on RAPDs and isozymes were lower: 0.74 and 0.68, respectively. While microsatellites are very useful for comparisons of closely related accessions, they are less suitable for studies involving less-related taxa. Isozymes provide interesting markers for species differentiation, but their use seems less appropriate for studies of within-species genetic variation. RAPDs can produce a large set of markers, which can be used for the evaluation of both between- and within-species genetic variation, more rapidly and easily than isozymes and microsatellites.
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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 | 0.001 | 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) | 0.000 | 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".