Geographical description and molecular characterization of genetic structure and diversity using a 6K SNP array in Turkish oat germplasm
Bibliographic record
Abstract
Cultivated oat (Avena sativa L. and Avena byzantina Koch) is native to Turkey, a secondary center of oat diversity. Oat breeding has received less attention relative to other cereals. In this work, the diversity of oat landraces collected from different regions of Turkey as well as obtained from different gene banks was investigated using 3293 high quality SNP markers. Expected heterozygosity (Hs), observed heterozygosity (Ho), inbreeding coefficient (Fis), and overall genetic diversity (Ht) of the oat gene pool were 0.22, 0.01, 0.96, and 0.38, respectively. The value of the genetic differentiation (Fst) metric for genetic structure was 0.41 and indicated that kinship was more of a determinant for population structure than the geographical provenance. The populations from different geographical regions shared a great proportion of genetic diversity. Clustering using model-based STRUCTURE, principal coordinates (PCoA), and neighbour-joining (NJ) algorithms were mostly comparable except for five discrepantly clustered accessions in NJ and PCoA relative to STRUCTURE, which can be attributed to the relatively reduced resolution power in the NJ and PCoA approaches. SNP markers partitioned all oat accessions into four main groups (A, B, C, and D) with 10 unclassified accessions. Some landraces were identical based on genetic distance and can represent duplications in gene banks. The data presented in this work represent the initial results on genetic diversity as investigated in Turkish oat, and are an important resource for systematicians, geneticists, and breeders interested in Turkish oat germplasm. These results are expected to open new opportunities for further studies in oat genomics and cultivar development.
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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".