Unweaving the population structure and genetic diversity of Canadian shrub willow
Bibliographic record
Abstract
Abstract Perennial shrub willow are increasingly being promoted in short-rotation coppice systems as biomass feedstocks, for phytoremediation applications, and for the diverse ecosystem services that can accrue. This renewed interest has led to widespread willow cultivation, particularly of non-native varieties. However, Canadian willow species have not been widely adopted and their inherent diversity has not yet been thoroughly investigated. In this study, 324 genotypes ofSalix famelicaandSalix eriocephalacollected from 33 sites of origin were analyzed using 26,016 single nucleotide polymorphisms to reveal patterns of population structure and genetic diversity. Analyses by Bayesian methods and principal component analysis detected five main clusters that appeared to be largely shaped by geoclimatic variables including mean annual precipitation and the number of frost-free days. The overall observed (HO) and expected (HE) heterozygosity were 0.126 and 0.179, respectively. An analysis of molecular variance revealed that the highest genetic variation occurred within genotypes (69%), while 8% of the variation existed among clusters and 23% between genotypes within clusters. These findings provide new insights into the extent of genetic variation that exists within native shrub willow species which could be leveraged in pan-Canadian willow breeding programs.
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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 | 0.001 | 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".