Impacts of drought on forest growth and regeneration following fire in southwestern Yukon, Canada
Bibliographic record
Abstract
The valleys of southwestern Yukon have a continental climate with average annual precipitation of <300 mm. In 1958, fires burned large areas of mature mixedwood forests dominated by white spruce (Picea glauca (Moench) Voss) in the valleys near Whitehorse. Since then, the burned areas have shown poor regeneration of spruce, but have been colonized by scattered clones of trembling aspen (Populus tremuloides Michx.) interspersed by grassland. The objective of the study was to examine the influence of climatic variation on forest growth and regeneration in the 1958 burn and the adjacent unburned forests. Tree-ring analysis was conducted on 50 aspen and 54 white spruce in 12 mature stands where these species were codominant, and on 147 regenerating aspen in the 1958 Takhini burn. The mature stands were uneven-aged and the patterns of growth variation for the aspen and spruce between 1944 and 2000 were similar. Growth of both species was most strongly related to variation in precipitation. The regenerating aspen had a wide age-class distribution (19592000) and their growth was also positively related to precipitation. The results indicate that these forests have been slow to regenerate after fire, and are vulnerable if the climate becomes drier under future global change.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".