Temporal predictability of wolf predation on livestock and wolf control in western North America
Bibliographic record
Abstract
Due primarily to predation on livestock (depredation), some livestock producers oppose wolf (Canis lupus) conservation, which is an important objective for large sectors of the public. Predicting depredation occurrence is difficult, yet necessary in order to prevent it. Better prediction of wolf depredation would also facilitate application of sound depre- dation management actions. In this talk, we analyze temporal trends in wolf depredation occurrence and related management actions. We gathered data from wolf depredation investigations in Idaho, Montana and Wyoming, U.S. from 1987 to 2003 and for Alberta, Canada from 1982 to 1996. All information was collected in partnerships with various interest groups, including ranchers and farmers, government authorities, environmental non-governmental organizations and universities. We showed that wolf attacks occurred with a seasonal pattern, reflecting the seasonality of livestock calving, grazing practices, and seasonal variation in energetic requirements of wolf packs. Seasonal wolf attacks were auto- correlated with lags of one year, indicating annual recurrence. Cross-correlation analyses showed that limited wolf control was rapidly employed as a short-term response to depre- dation, and was not designed to decrease wolf depredation at a regional scale or in the long term. Available data allowed for an analysis of the U.S. compensation program, another typical depredation management response. Livestock producers were normally compen- sated within three months following depredation events. The timing of refunding was com- parable or shorter than other compensation programs for carnivore damage employed in other regions. Our findings indicated that compensation programs could be coupled with incentives for proactive management focused on reducing losses during high-depredation seasons.
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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 | 0.001 |
| 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".