Seasonal Wolf Predation in a Multi-Prey System in West-Central Alberta
Bibliographic record
Abstract
Estimating annual wolf kill rates and composition is important for assessing the impact of wolves on their prey and managing wolf-ungulate dynamics. Most studies have focused on kill rates of wolves in winter or single-ungulate dominated systems. I used high intensity GPS tracking combined with scat analysis to explored intra- and inter-seasonal variations in kill rates and prey composition of wolves in a multi-prey ungulate population. I found wolves in summer selected for neonate prey of all species with deer comprising the greatest proportion of both adult and neonate prey. Summer kill rates (0.21 ungulates/ adult wolf/day) were among the highest (~1.5-2.5 times) reported in the literature and were 2.5 times higher than winter rates (0.08+0.02), when wolves killed a greater diversity of predominately adult prey. Summer biomass consumption rates (4.22+0.36 kg/adult equivalent wolf/day) were lower than in winter (7.93+4.08), when wolves were less food limited. Seasonal differences in kill rates would have lead to significant underestimates (~29%) of annual kill rates when based on winter information only.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".