Local characteristics of the standing genetic diversity of European beech with high within-region differentiation at the eastern part of the range
Bibliographic record
Abstract
Developing “climate smart forestry” (CSF) indicators in mountain forest regions requires collection and evaluation of local data and their attributes. Genetic resources are listed among the core indicators for forest biological diversity. This study is a report on the evaluation of the standing genetic diversity within and across 12 pure beech stands (Fagus sylvatica L.) established within the CLIMO (CLImate Smart Forestry in MOuntain Regions) project, using nuclear microsatellite markers. The sampling sites were set along the species’ distribution range, including the Balkan region and extending towards the Iberian Peninsula. Cores or leaves from 20 to 23 old, mature trees per plot were sampled for DNA analysis. Genetic diversity indices were high across the range (HE = 0.74−0.81) with the highest in the Bosnian Mountains. Genetic divergence increased significantly with the geographical distance (Mantel test: r = 0.81, p < 0.001). Most of the stands exhibited an excess of heterozygotes, with the highest value at the Hungarian site (HO/HE = 1.177), where beech persists close to the eastern xeric limit of the species’ distribution. STRUCTURE revealed within-region differentiation in the Balkan Peninsula, where the Bulgarian stand was the most outstanding. The genetic parameters of each stand could be assessed as a resource for CSF indicators interpreted especially at the local level.
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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".