Grey wolf selection for moose calves and factors influencing prey species consumption in southeastern Manitoba
Bibliographic record
Abstract
Moose populations in southern Manitoba have declined in recent years, and although the cause of the decline is yet unknown, wolf predation has been suggested as a potential contributing factor. We used fecal analysis combined with telemetry data to test the influences of social structure, relative prey abundance, and season on wolf consumption of moose and other prey species. We tested for influences of social structure, relative prey abundance, and summer time period specifically on consumption of moose calves in summer, and compared consumption of moose calves to the relative occurrence of calves in the overall moose population. Wolves hunting in a pack were more likely to consume moose than solitary wolves, while solitary wolves were more likely to consume other non-ungulate prey. Solitary wolves were more likely to eat deer in areas where deer were more abundant, but we found no difference in consumption of moose by solitary wolves between areas of greater moose abundance. Beaver were consumed more in summer, but consumption of other prey species did not differ seasonally. We found no effect of social structure, relative prey abundance, or summer time period on consumption of moose calves. Wolves killed calves preferentially, in excess of their relative abundance, only in late summer. Management of wolves aimed at decreasing wolf numbers in southeastern Manitoba may also reduce predation on adult moose by decreasing pack sizes and interrupting the social organization.
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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 | 0.000 | 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".