Combining microsatellites, growth, and adaptive traits for managing in situ genetic resources of<i>Eucalyptus urophylla</i>
Bibliographic record
Abstract
Genetic diversity and structure of 17 populations representative of the natural range of Eucalyptus urophylla S.T. Blake in Indonesia were analysed with 10 microsatellite markers. Two provenance and progeny trials, using the same populations, were established in the Republic of the Congo and analysed for growth and survival at 37 months. Observed microsatellite heterozygosity (Ho) was moderate to high within populations (Ho= 0.51–0.72). The index of fixation (FIS) was significantly different from zero for all populations (FIS= 0.13–0.31) and possibly resulted from a Wahlund effect. The differentiation parameter was low (FST= 0.04) and not significantly different from zero, which can be explained by effective gene flow via pollen. The genetic variances within and among provenances for survival and growth traits were significantly different from zero, representing 13%–23% and 14%–50% of the total variation, respectively. The differentiation between populations based on quantitative traits was marked (QST= 0.07–0.33). A negative relationship between altitude of the seed source and its performance in the Congo was observed (R2= 0.59–0.67) and could be explained by the effect of natural selection along the altitudinal gradient. This marked differentiation for quantitative traits despite high apparent gene flow results in a clinal variation, which suggests the use of altitude of seed source as an important principle for in situ or ex situ management of E. urophylla genetic resources.
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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 | 0.001 | 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".