Genetic diversity and population structure of boreal white spruce (<i>Picea glauca</i>) in pristine conifer-dominated and mixedwood forest stands
Bibliographic record
Abstract
White spruce ( Picea glauca (Moench) Voss) is a characteristic primary species of the Canadian boreal forest region, where it occurs in conifer-dominated and mixedwood forest types. Genetic diversity and population structure of white spruce may differ between the conifer-dominated and mixedwood forest types owing to the inherent differences in stand structure and dynamics. The objective of our study was to determine genetic diversity and population structure of pristine white spruce stands as they occur in conifer-dominated and mixedwood forest types at the EMEND (Ecosystem Management Emulating Natural Disturbance) study sites in northern Alberta. Nuclear microsatellite DNA markers were used to examine genetic diversity and population structure of 16 pristine natural old-growth (≥100 years) white spruce stands (subpopulations) of fire origin; 8 from conifer-dominated and 8 from neighboring mixedwood forest types. High levels of genetic diversity were observed, as expected. The genetic diversity and genetic constitution of white spruce were similar between the conifer-dominated and mixedwood forest types. Most of the genetic variation resided within subpopulations, with only about 2% genetic differentiation detected among 16 subpopulations as well as among 8 subpopulations within the same forest type. The mean genetic distances among subpopulations within and between the forest types were similar. Our study suggests that white spruce genetic resources are similar in the conifer-dominated and mixedwood forest types located in the EMEND study area in northern Alberta, and it provides the benchmarks for determining and monitoring the genetic diversity impacts of forest harvesting and forest fires.
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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".