Impact of biased sex ratio on the genetic diversity, structure, and differentiation of <i>Populus nigra</i> (European black poplar)
Bibliographic record
Abstract
Effective population size is a crucial concept of conservation biology. It is reduced by biased sex ratio, consequently causing loss of genetic variation. To evaluate genetic diversity related to gender, and investigate the possible effects of biased sex ratio, we analyzed available microsatellite DNA markers from 120 samples of Populus nigra L. (European black poplar) originating from five geographical regions in Turkey. Using 12 microsatellite markers, we detected 60 clones of the same genotype, out of 120 trees. The clone genotype was observed both in males and females, which might suggest that P. nigra deviates from dioecism. Three genetic clusters were detected, two of which possibly correspond to commercially available trees. Overall allelic richness was found to be similar for both genders, whereas heterozygosity was slightly higher in males. Additionally, a simulation software prototype was developed to see the effects of sex ratio on diversity and allele frequency trends in future generations, given the available molecular data. Results showed that if biased sex ratio persists, allele loss and fixation might occur in a higher rate, causing loss of variation.
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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 | 0.000 | 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) | 0.001 | 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".