Gray wolf (<i>Canis lupus</i>) predation patterns following recent recolonization in a multi-predator, multi-prey system
Bibliographic record
Abstract
Predator–prey interactions are among the most fundamental of ecological relationships. Recolonizing gray wolf (Canis lupus Linnaeus, 1758) populations present new challenges for wildlife management in multi-prey, multi-carnivore systems. We documented diet composition and kill rates for wolves in a recently recolonized area over winter and summer seasons (2014–2015). Elk (Cervus canadensis Erxleben, 1777) were the primary ungulate prey (63%) located at wolf kill sites. Deer (mule deer (Odocoileus hemionus (Rafinesque, 1817)) and white-tailed deer (Odocoileus virginianus (Zimmermann, 1780))) were less prevalent than elk in wolf diets, but the amount of deer in diets (40%–50%) varied by pack and season. Juvenile elk were the most prevalent class of prey in wolf diets during summer (63.3%) and winter (36.3%), with adult elk (32.5%) observed nearly as often as juveniles in winter. Kill rates varied by season, with rates 2.3 times higher in summer (mean = 3.5 ungulates/week per pack) than winter (mean = 1.5 ungulates/week per pack), consistent with increased availability and use of neonate prey. Prey biomass acquisition did not vary by pack or season (summer = 243 kg/week per pack; winter = 182 kg/week per pack). Our study quantified predation patterns for a recolonizing wolf population, and patterns that we documented were similar to other multi-prey systems in North America.
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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 | 0.000 |
| 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 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".