Geographical and historical determinants of microsatellite variation in Eucalyptus pilularis
Bibliographic record
Abstract
Geographically distributed genetic variation is expected in species that have wide latitudinal and habitat ranges, like Eucalyptus pilularis Sm. Coastal and inland ecotypes of this tall forest tree have been distinguished in genecological studies, but patterns of regionally distributed quantitative variation are weak. At the coarsest level, variation of 12 microsatellite markers divided a rangewide sample of 424 E. pilularis trees into two zones: the region to the south of Sydney forming one zone and regions to the north forming another. Genetic structuring did not correspond with ecotypes but rather with a biogeographic division, suggesting an imprint of historical isolation. Typical and uniform levels of genetic diversity (He= 0.78 ± 0.02 (mean ± SE)) were found across 10 geographic regions. Genetic structuring by regions (PhiRT = 3%), by localities within regions (PhiPT = 2%), between coastal and inland provenances (PhiPT = 2%), or due to isolation by distance was subtle. These observations, along with the lack of evidence for bottlenecks, suggested genetic cohesion within zones due to gene flow and historically large population sizes. The low levels of diversity and poor growth performance of the Fraser Island ecotype were better explained by recent colonization and adaptation than by genetic isolation, since there was no evidence of inbreeding.
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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 | 0.001 |
| 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".