Factors affecting gray wolf (<i>Canis lupus</i>) encounter rate with elk (<i>Cervus elaphus</i>) in Yellowstone National Park
Bibliographic record
Abstract
Despite encounter rates being a key component of kill rate, few studies of large carnivore predation have quantified encounter rates with prey, the factors that influence them, and the relationship between encounter rate and kill rate. The study’s primary motivation was to determine the relationship between prey density and encounter rate in understanding the mechanism behind the functional response. Elk (Cervus elaphus Linnaeus, 1758) population decline and variable weather in northern Yellowstone National Park provided an opportunity to examine how these factors influenced wolf (Canis lupus Linnaeus, 1758) encounter rates with elk. We explored how factors associated with wolf kill rate and encounter rate in other systems (season, elk density, elk group density, average elk group size, snow depth, wolf pack size, and territory size) influenced wolf–elk encounter rate in Yellowstone National Park. Elk density was the only factor significantly correlated with wolf–elk encounter rate, and we found a nonlinear density-dependent relationship that may be a mechanism for a functional response in this system. Encounter rate was correlated with number of elk killed during early winter but not late winter. Weak effects of snow depth and elk group size on encounter rate suggest that these factors influence kill rate via hunting success because kill rate is the product of hunting success and encounter rate.
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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 | 0.001 | 0.000 |
| 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".