Impacts on declining moose populations in southeastern Manitoba
Bibliographic record
Abstract
Moose (Alces alces) populations in eastern and central North America have declined in many parts of their southern range. Many potential impacts on moose have been suggested as contributing to moose declines, including changing habitat disturbance regimes and enhanced disease transmission through increasing deer populations. We examined factors affecting moose in Game Hunting Area (GHA) 26 in southeastern Manitoba, where moose populations have declined substantially, by comparing provincial aerial survey data with features of the landscape. Moose were more likely to be found in areas with high logging (>25%) and recent forest fires (within the past 30 years), indicating that moose respond favorably to habitat disturbances. The presence of roads did not affect the likelihood of moose presence. Moose were negatively impacted by white-tailed deer (Odocoileus virginianus). We used model selection to determine the variables most important for predicting the presence of moose in GHA 26. The best model included the presence of deer, logging, and forest fires. Among the variables considered, deer presence had the highest relative importance. This study suggests that to increase moose numbers, controlled burns and potential logging areas should be considered as ways to produce new habitat and plant growth for moose in the area. Managing the deer population also could control the effect of the deer brain worm (Parelaphostrongylus tenuis) on the moose population in GHA 26.
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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 | 0.001 |
| 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".