Predicting risk of livestock depredation by wolves in southwestern Alberta
Bibliographic record
Abstract
Wolves can potentially prey on all ungulates within their distributional range, including domestic livestock. The potential for conflict between wolves and humans therefore exists especially in rural areas where livestock production is a major economic activity, such as southwestern Alberta. Limited studies have examined factors that predispose livestock to depredation by wolves, and none occurred in southwestern Alberta. The purpose of this study was to determine the spatial relationships between habitat characteristics, human use and wolf depredation on livestock in southwestern Alberta. The goal is to use these characteristics as predictors for areas at risk. We used Geographic Information Systems (GIS) to examine the effects of vegetation productivity, geography, and proximity to roads, rivers, and cover on predicting livestock depredation by wolves. Binary logistic regression analyses, ranked using Akaike Information Criteria (AIC), were used to determine what variables were best at explaining depredation occurrence. On private lands, greenness and elevation were important variables in the best logistic regression model (y = -22.366 + 0.009(elev) + 0.024(green)). These variables were also significantly different between depredated and random sites (elevation (tcrit = 1.97, p = 0.0035), greenness (P tcrit = 1.97, p = 4.40E-08)). Our results indicate that ranches (and land within an 8-km buffer of them) in proximity to the Rocky Mountains and in areas of higher vegetation productivity are at risk of depredation by wolves.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".