Estimating the genetic diversity and structure of <i>Quercus trojana</i> Webb populations in Italy by SSRs: implications for management and conservation
Bibliographic record
Abstract
Studying the genetic diversity and structure of the current forest populations is essential for evaluating the ability to survive future biotic and abiotic changes and planning conservation strategies. Quercus trojana Webb is an eastern Mediterranean tree species with a fragmented distribution range, and its westernmost outposts are located in southern Italy. The demand for timber and cropland over the centuries has severely reduced its occurrence in this part of the range. We assessed the genetic diversity and structure of the extant Italian populations of Q. trojana and derived conservation guidelines. A total of 322 samples were genotyped with six polymorphic nuclear microsatellite markers. A high genetic diversity in all populations, two main gene pools, and a highly divergent single population were observed. Based on the allelic richness and heterozygosity estimation, we identified populations that can be considered as valuable source material for conservation programs and those requiring adequate measures to reestablish gene flow and reduce fragmentation. Finally, a comparison with a set of eastern Mediterranean samples indicated a relationship between the Italian and the Greek gene pools. The need to protect these marginal, disjunct populations was further reinforced.
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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 | 0.002 | 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".