Differential wolf-pack-size persistence and the role of risk when hunting dangerous prey
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Risk to predators hunting dangerous prey is an emerging area of research and could account for possible persistent differences in gray wolf ( Canis lupus ) pack sizes. We documented significant differences in long-term wolf-pack-size averages and variation in the Superior National Forest (SNF), Denali National Park and Preserve, Yellowstone National Park, and Yukon, Canada (). The SNF differences could be related to the wolves’ risk when hunting primary prey, for those packs () hunting moose ( Alces americanus ) were significantly larger than those () hunting white-tailed deer ( Odocoileus virginianus ) (, ). Our data support the hypothesis that differential pack-size persistence may be perpetuated by differences in primary prey riskiness to wolves, and we highlight two important extensions of this idea: (1) the potential for wolves to provision and defend injured packmates from other wolves and (2) the importance of less-risky, buffer prey to pack-size persistence and year-to-year variation.
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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 | 0.000 |
| 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 it