Evidence for low genetic diversity and metapopulation structure in Canada yew (<i>Taxus canadensis</i>): considerations for conservation
Bibliographic record
Abstract
Canada yew (Taxus canadensis Marsh.) is a gymnosperm that grows in the understory of mixed and deciduous forests of northeastern North America. This shrub had no economic importance until the discovery of paclitaxel, or TAXOL®, which is a compound found in plant tissue and used in cancer treatment. With the intensifying harvesting pressure on natural populations of this species, the natural gene pool might be affected. The objective of this study was to estimate the levels of genetic diversity and population structure in Canada yew, before any sizeable effects resulting from harvesting appear. Six natural populations of Canada yew were sampled in Quebec. Genetic diversity was estimated at 22 loci coding for 12 enzyme systems. At the population level, the number of alleles per locus was 1.32, the percentage of polymorphic loci was 26.5%, and the observed heterozygosity was 0.102. These results show that Canada yew is genetically less diverse than other yew species and the great majority of gymnosperms. However, the amount of population differentiation was substantially higher (FST= 10.2%) than that for other conifer and tree species growing in the boreal-temperate zone. Hypotheses related to the biogeography of the species and a likely metapopulation structure are proposed to explain the observed trends.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".