Spatial Search and Efficiency Rates as Components of Wolf Predation Risk
Bibliographic record
Abstract
Anthropogenic linear features are hypothesized to increase wolf (Canis lupus) predation risk for a threatened ungulate, woodland caribou (Rangifer tarandus caribou). Previous research has shown that these features are selected by wolves while searching for prey, but their effect on the net efficiency of predation, measured in kills per day, has not been addressed. We use resource selection and proportional hazards modeling to assess the spatial drivers of both search and efficiency rates of wolf predation in a multi-prey system. Topographic variation consistently affected wolf search rates and the predation efficiency of wolves while searching. However, the effects of anthropogenic footprint upon the total predation risk imposed by wolves were mediated solely by changes to wolf search rate; wolf predation efficiency generally did not change with proximity to anthropogenic linear features as previously hypothesized. Predicted models of the cumulative hazard encountered by wolves validated well with among-pack variation in kill rates, suggesting that spatial hazard models allow the scaling up of local heterogeneity to population-level dynamics. Lastly, we estimated an integrated spatial model of relative predation risk as the product of both search and efficiency rates, which captured the distinct contributions of spatial heterogeneity to each component of risk.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".