Post-fire conifer regrowth in upper subalpine Grouseberry (<i>Vaccinium scoparium</i>) habitat used by Grizzly Bear (<i>Ursus arctos</i>) in Banff National Park, Alberta
Bibliographic record
Abstract
Grouseberry (Vaccinium scoparium) communities in many open, upper subalpine sites within Banff National Park have abundant conifer seedlings, saplings, and small trees. Because fruit production typically decreases with increasing forest canopy cover, we predict that infilling by conifers will lead to reduced Grouseberry fruit availability for wildlife, including Grizzly Bear (Ursus arctos), a Threatened species in Alberta. We studied conifer recruitment in Grouseberry communities within recent (2001 prescribed fire) and old (1920 and 1889) burns in Banff National Park during 2008–2022. Most (55%) of the conifers in our plots were Engelmann Spruce (Picea engelmannii); an additional 34% were Subalpine Fir (Abies lasiocarpa), 11%, Lodgepole Pine (Pinus contorta), and <1%, Subalpine Larch (Larix lyallii). The density of small conifers (height >30 cm, diameter <10 cm) did not vary in communities that were 21, 102, and 133 years post-fire, but was lower in the 2001 burn 6–7 years post-fire. The mean density of small conifers in the 2001 prescribed burn increased from 15 ± 18 (SD) stems/100 m2 in 2007–2008 (6–7 years post-fire) to 73 ± 67 stems/100 m2 in 2022 (21 years post-fire). Conifer ingrowth has the potential to overgrow and shade Grouseberry habitat, with declining fruit production a predicted consequence. However, in Banff National Park, where lightning-caused fires are rare, managers have the option of applying prescribed fire to create openings in the upper subalpine forest and thus enhance fruit production in Grouseberry and other fruiting shrubs important to wildlife.
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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 | 0.001 | 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".