Population genetic variation characterization of the boreal tree Acer ginnala in Northern China
Bibliographic record
Abstract
Genetic diversity and differentiation are revealed particularly through spatio-temporal environmental heterogeneity. Acer ginnala, as a deciduous shrub/small tree, is a foundation species in many terrestrial ecosystems of Northern China. Owing to its increased use as an economic resource, this species has been in the vulnerability. Therefore, the elucidations of the genetic differentiation and influence of environmental factors on A. ginnala are very critical for its management and future utilization strategies. In this study, high genetic diversity and differentiation occurred in A. ginnala, which might be resulted from its pollination mechanism and species characteristics. Compared with the species level, relatively low genetic diversity was detected at the population level that might be the cause for its vulnerability. There was no significant relationship between genetic and geographical distances, while a significant correlation existed between genetic and environmental distances. Among nineteen climate variables, Annual Mean Temperature (bio1), Mean Diurnal Range (bio2), Isothermality (bio3), Temperature Seasonality (bio4), Precipitation of Wettest Month (bio13), Precipitation Seasonality (bio15), and Precipitation of Warmest Quarter (bio18) could explain the substantial levels of genetic variation (> 40%) in this species. The A. ginnala populations were isolated into multi-subpopulations by the heterogeneous climate conditions, which subsequently promoted the genetic divergence. Climatic heterogeneity played an important role in the pattern of genetic differentiation and population distribution of A. ginnala across a relatively wide range in Northern China. These would provide some clues for the conservation and management of this vulnerable 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 | 0.000 |
| 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) | 0.000 | 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".