Kill rate by wolves on moose in the Yukon
Bibliographic record
Abstract
We studied the kill rate by wolves (Canis lupus) after a large-scale wolf removal when populations of wolves, moose (Alces alces), and woodland caribou (Rangifer tarandus caribou) were all increasing. We followed a total of 21 wolf packs for 4 winters, measuring prey selection, kill rates, and ecological factors that could influence killing behavior. Wolf predation was found to be mainly additive on both moose and caribou populations. Kill rates by individual wolves were inversely related to pack size and unrelated to prey density or snow depth. Scavenging by ravens decreased the amount of prey biomass available for wolves to consume, especially for wolves in smaller packs. The kill rate by wolves on moose calves was not related to the number of calves available each winter. Wolves did not show a strong switching response away from moose as the ratio of caribou to moose increased in winter. The predation rate by wolves on moose was best modeled by the number and size of packs wolves were organized into each winter.
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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 | 0.000 | 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) | 0.000 | 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".