Genetic variation within and between winter wheat genotypes from Turkey, Kazakhstan, and Europe as determined by nucleotide-binding-site profiling
Bibliographic record
Abstract
The genetic diversity within wheat breeding programs across Turkey and Kazakhstan was compared with a selection of European cultivars that represented the genetic diversity across eight European countries and six decades of wheat breeding. To focus the measure of genetic diversity on that relevant to disease-resistant phenotypes, nucleotide-binding-site (NBS) profiling was used to detect polymorphisms associated with the NBS motifs found within the NBS--leucine-rich repeat (LRR) class of resistance (R) genes. Cereal-specific NBS primers, designed specifically to the conserved NBS motifs found within cereal R-genes, provided distinct NBS profiles. Although the genetic diversity associated with NBS motifs was only slightly higher within the Eastern wheat genotypes, the NBS profiles produced by Eastern and European wheat lines differed considerably. Structure analysis divided the wheat genotypes into four groups, which compared well with the origin of the wheat genotypes. The highest levels of genetic diversity were seen for the wheat genotypes from the Genetic Resource Collection held in Ankara, Turkey, as wheat genotypes within breeding programs were genetically more similar. The wheat genotypes from Kazakhstan were the most similar to the European cultivars, reflecting the significant number of eastern European cultivars used in the breeding program in Kazakhstan. In general, the NBS profiles suggested that NBS-LRR R-gene usage in winter wheat breeding in Turkey and Kazakhstan differed from that deployed in European cultivars.
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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".