Hierarchical predation: wolf (<i>Canis lupus</i>) selection along hunt paths and at kill sites
Bibliographic record
Abstract
Predation is a hierarchical process whereby a predator is constrained to killing prey within the area they select while hunting. We demonstrate the hierarchical nature of predation using movement data from six GPS-collared wolves ( Canis lupus L., 1758) in the Rocky Mountains of Alberta, Canada, by coupling the kill locations of their ungulate prey with their preceding hunt path. Selection of where to hunt constrained the characteristics influencing where wolves killed within hunt paths. Specifically, wolves selected to hunt where prey densities were higher than the mean density for their territories, but prey densities were not related to kill site locations within the selected hunt path. Wolves selected to hunt in open valleys and near habitat edges, where prey may be most predictable, detectable, or vulnerable, which may have been reinforced by a higher likelihood of killing within these characteristics along hunt paths. In contrast, wolves selected to hunt relatively farther from frozen water bodies and closer to well sites than kill site locations, indicating different processes were occurring during the hunting and killing phases. Treating predation as a hierarchical sequence will ensure the role of prey and landscape characteristics on the processes of predation are not over- or under-emphasized by decoupling kill sites from hunt paths, which will lead to a better mechanistic understanding of predation in heterogeneous environments.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".