Importance of scale, season, and forage availability for understanding the use of recent burns by woodland caribou during winter
Bibliographic record
Abstract
During winter, woodland caribou ( Rangifer tarandus caribou) may avoid burned forest for up to 60 years. Typically, that is the time required for lichens to recover following fire. We examined the response of caribou of the Klaza population (west-central Yukon, Canada) to recent burns (≤50 years) during winter. We quantified resource selection of individual caribou across the winter range and use of habitats that were adjacent to or within burns. Typically, caribou selected or used areas with greater density of terrestrial lichen. There was considerable inter-animal variability, but in some season-years caribou selected burned habitat with stronger selection of relatively small burns. Approximately 6.2% of GPS-collar locations were located outside but within 500 m of the boundary of a recent burn and 5.6% of locations occurred within a burn. During late winter, when snow was deeper, caribou demonstrated greater avoidance of burns. Our results suggest that the relationship between caribou and burns is dynamic. Caribou will use recent burns, but such relationships are complicated by cumulative landscape change. It is important to recognise plasticity in behaviour when developing land-use strategies that represent the multi-year, seasonal requirements of the population.
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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 | 0.001 | 0.001 |
| 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".