Can Wolf Predation Immediately Alter the Foraging Behavior of Beavers?: Video of a Wolf Killing a Foraging Beaver
Bibliographic record
Abstract
) interactions has largely been derived from indirect observations due to the cryptic nature of wolves and the densely forested areas where they usually kill beavers. In September 2023, we captured a video via remote camera of a wolf killing an adult beaver that was foraging on a feeding trail. This observation provides insight into how wolves may prevent beavers from reaching water after an attack is initiated, as well as how beavers attempt to escape once attacked. The camera also recorded the number of beavers foraging before and after the kill, providing a unique opportunity to observe the foraging behavior of the surviving beavers. The camera recorded videos on the trail for 11 nights before the predation and 37 nights after the predation. The time beavers spent on the feeding trail declined by 96% following predation. Although we present just a single observation, it raises an interesting question: is it possible or even plausible to think wolves might immediately alter where or the extent to which beavers forage through predation? We provide a detailed discussion on possibilities and highlight areas for future research.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".