Influence of prescribed fire on Stone's sheep and Rocky Mountain elk: Forage characteristics and resource separation.
Bibliographic record
Abstract
For over 30 years prescribed fire has been used as a management tool to enhance ungulate habitat in northeastern British Columbia (BC), where up to 7,800 ha are burned annually. Yet relatively few studies have quantified the role of fire on both plant and animal response, and whether it enables competition between focal grazing species such as Stone's sheep (Ovis dalli stonei) and elk (Cervus elaphus). Seven prescribed burns (150-1,000 ha) were implemented in the spring of 2010 and 2011 in the Besa-Prophet area of northern BC. I examined the response of Stone's sheep and elk to seasonal changes in forage quantity and quality by elevation in treatment versus control areas. I monitored vegetation and fecal pellet transects at a fine scale and used Landsat imagery, survey flights and GPS telemetry at a landscape scale. By one year after burning, forage digestibility and rates of forage growth were higher on burned than unburned areas. At both scales Stone's sheep and elk always used burns more than control areas in winter. Stone's sheep and elk appeared to partition their use of the landscape through topography and land cover. Increased use of burned areas suggests that prescribed fire enhanced habitat value for grazing ungulates in the short-term. By altering animal distributions, however, the use of prescribed fire has the potential to change complex predator-prey interactions in northern BC. --Leaf ii.
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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 | 0.000 | 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".