The influence of environmental heterogeneity on the morphological and genetic diversity of Circaea lutetiana (Onagraceae) in Hyrcanian forests
Bibliographic record
Abstract
Environmental gradients are important factors that can potentially influence the genetic diversity and differentiation of populations. The present study examines the effect of environmental heterogeneity of the Hyrcanian forests on populations of Circaea lutetiana L. (Onagraceae). Using morphometrics, scanning electron microscopy (SEM) of leaf epidermis, and molecular markers, we analyzed genetic diversity and differentiation among nine populations from environmentally divergent habitats. Three different gene pools were observed. Our results indicate that the genetic structure is significantly correlated to environmental factors, but not to the geographical distance. Genetic clustering in C. lutetiana is affected by temperature, humidity, elevation, and average annual rainfall. Overall, our data indicate that gene flow does not contribute to explaining spatial patterns of genetic structure and the adaptation to the environment is the main factor shaping the genetic structure of the C. lutetiana populations. The populations belonging to each of the three gene pools have similarities in microclimate parameters, despite their geographical proximity, and populations from the same genetic pool can be up to 470 km apart. This evidence, as well as morphological and genetic similarities of the populations with greater geographical distance, suggest the possibility of cryptic speciation in this species.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.000 | 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".