Predation rate by wolves on the Porcupine caribou herd
Bibliographic record
Abstract
Large migratory catibou {Rangifer tarandus) herds in the Arctic tend to be cyclic, and population trends are mainly driven by changes in forage or weather events, not by predation. We estimated daily kill rate by wolves on adult caribou in winter, then constructed a time and space dependent model to estimate annual wolf (Canis lupus) predation rate (P annual) on adult Porcupine caribou. Our model adjusts predation seasonally depending on caribou distribution: Pannual = SIGMAdaily* W *Ap(2)*Dp. In our model we assumed that wolves killed adult caribou at a constant rate (Kdaily, 0.08 caribou wolf1 day1) based on our studies and elsewhere; that wolf density (W) doubled to 6 wolves 1000 km2-1 on all seasonal ranges; and that the average area occupied by the Porcupine caribou herd (PCH) in eight seasonal life cycle periods (Dp ) was two times gteater than the area described by the outer boundaries of telemetry data (Ap /1000 km2). Results from our model projected that wolves kill about 7600 adult caribou each year, regardless of herd size. The model estimated that wolves removed 5.8 to 7.4% of adult caribou as the herd declined in the 1990s. Our predation rate model supports the hypothesis of Bergerud that spacing away by caribou is an effective anti-predatory strategy that greatly reduces wolf predation on adult caribou in the spring and summer.
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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 | 0.001 | 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".