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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".