ddRAD-seq generated genomic SNP dataset of Central and Southeast European Turkey oak (Quercus cerris L.) populations
Bibliographic record
Abstract
Abstract Turkey oak (Quercus cerris L.) is one of the most ecologically and economically significant deciduous tree species in the Central and Southeast European regions. The species has long been known to exhibit high levels of genetic and phenotypic variation. Recent climate response predictions for Turkey oak suggest a significant distribution extension in Europe under climate change. Since Turkey oak has relative drought-tolerant behaviour, it is regarded as a potential alternative for other forest tree species during forestry climate adaptation efforts, not only in its native regions but also in Western Europe. For this reason, the survey of existing genetic variability, genetic resources, and adaptability of this species has great significance. Next-generation sequencing approaches, such as ddRAD-seq (Double digest restriction-site associated DNA sequencing), allow the attainment of high-resolution genome-wide single nucleotide polymorphisms (SNPs). This study provides the first highly variable genome-wide SNP data for Turkey oak generated by ddRAD-seq. The dataset comprises 17 607 de novo and 26 059 reference mapped SNPs for 88 individuals from eight populations, two from Bulgaria, one from Kosovo, and five from Hungary. Reference mapping was carried out by using cork oak’s (Quercus suber L.) reference genome. The obtained high-resolution genome-wide markers are suitable for investigating selection and local adaptation and inferring genetic diversity, differentiation, and population structure. The dataset is accessible at: https://doi.org/10.5281/zenodo.8091252
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".