Limited immediate effects of wildfire on Alaska moose harvest
Bibliographic record
Abstract
Qualitative studies have suggested that forest changes following a wildfire can challenge a hunter’s ability to harvest big game, such as moose ( Alces alces). Quantitative effects have not been estimated. Given the increasing prevalence of wildfires, the strong linkages between wildfire and moose habitat, and the importance of moose to the people of the boreal region of North America, our goal was to assess if and how moose harvest patterns changed immediately following a wildfire. To address that goal, we used 36 years (1984–2019) of spatially explicit wildfire and moose harvest data in Alaska to compare moose harvest variables the year before and year after a wildfire occurred. With a few exceptions, the number of hunters, kills, and success rates were similar ( p > 0.05, effect size < 0.3) between pre- and post-wildfire years. We estimated a weak to moderate effect on change in moose hunter numbers, kills, and success rate in only a small percentage (1.5%) of wildfires that burned a very large proportion (>38%) of a moose harvest reporting unit. Our findings suggest that wildfire has not caused a clear and functional quantitative effect on hunters’ ability to harvest moose in Alaska.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".