Comparison of pre-fire and post-fire space use reveals varied responses by woodland caribou (<i>Rangifer tarandus caribou</i>) in the Boreal Shield
Bibliographic record
Abstract
By regulating successional dynamics in Canada’s boreal forest, fires can affect the distribution of the Threatened woodland caribou (Rangifer tarandus caribou (Gmelin, 1788)). Caribou tend to avoid areas burned within the last 40 years; however, few studies have compared pre-fire and post-fire caribou observations. In this study, we used caribou GPS locations from the Boreal Shield of Saskatchewan, Canada, to assess the short-term response of caribou to areas that burned while they were collared (hereafter recent burns). We used a “before–after, control–impact” design to compare the overlap of pre-fire and post-fire seasonal home ranges to the overlap of year-to-year seasonal home ranges. Caribou rarely encountered recent burns and when they did, they adjusted their space use in variable and complex ways that were largely indistinguishable from regular, interannual variation. Caribou tended to reduce use of recent burns in summer–autumn and winter, but not during the calving season, in some cases shifting their home range to incorporate more burned habitat. We conclude that recently burned areas (<5 years) may provide habitat value to woodland caribou, particularly during the calving season, requiring a more flexible approach to interpret fire in habitat management strategies.
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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 | 0.000 |
| Science and technology studies | 0.000 | 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".