Climate change effects on deer and moose in the Midwest
Bibliographic record
Abstract
ABSTRACT Climate change is an increasing concern for wildlife managers across the United States and Canada. Because climate change may alter populations and harvest dynamics of key species in the region, midwestern states have identified the effects of climate change on ungulates as a priority research area. We conducted a literature review of projected climate change in the Midwest and the potential effects on white‐tailed deer ( Odocoileus virginianus ) and moose ( Alces alces ). Warmer temperatures and decreasing snowpack in the region favor survival of white‐tailed deer. In contrast, moose may become physiologically stressed in response to warming, and increasing deer populations spreading disease will exacerbate the problem. Although there is some uncertainty about exactly how the climate will change, and to what degree, robust projections suggest that deer populations will increase in response to climate change and moose populations will decrease. Managers can begin preparing for these changes by proactively creating management plans that take this into account. Published 2019. This article is a U.S. Government work and is in the public domain in the USA. The Journal of Wildlife Management published by Wiley Periodicals, Inc. on behalf of The Wildlife Society
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".