MOOSE USE OF THE MOUNT MCALLISTER BURN IN NORTH-CENTRAL BRITISH COLUMBIA: INFLUENCE OF BURN SEVERITY AND SOIL MOISTURE
Bibliographic record
Abstract
The influence of recent wildfires in British Columbia (BC) on moose habitat and its use by moose are understudied, as are prescribed burning strategies that can be used to enhance moose habitat. Our objective was to investigate how 3 classes of fire severity (high, medium, low) interact with 3 soil moisture regimes (hydric, mesic, xeric) in determining how moose use post-fire habitat. In north-central BC, we studied moose use at 2 different spatial levels in the 5-year-old, 26,500 ha Mt. McAllister burn. At the site level, we estimated the density of fecal pellet groups and the percent of plants browsed by moose within plots of varying burn severity and soil moisture. At the landscape level, we investigated use from GPS locations of 7 radio-collared female moose at 3 orders of selection: we compared: 1) randomly distributed locations within the home range to randomly distributed locations throughout the entire burn (2nd order of selection); 2) use locations to randomly distributed potential locations within the home range (3rd order of selection); and daily use locations with potential movement locations (4th order of selection). At the site level, moose used areas of low/medium fire severity and hydric soil moisture. At the landscape level, moose preferred areas of medium fire severity at the daily order, and low/medium fire severity at both the home range and burn orders of selection. Our findings highlight that moose use of post-fire habitat varied by spatial scale and by order of selection and that researchers assessing use of burns by moose should consider multiple levels of investigation. Prescribed burning to enhance moose habitat should focus on low/medium fire severity at sites with mesic soil moisture.
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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".