Agricultural lands as ecological traps for grizzly bears
Bibliographic record
Abstract
Abstract Human–carnivore conflicts on agricultural lands are a global conservation issue affecting carnivore population viability, and human safety and livelihoods. Locations of conflicts are influenced by both human presence and carnivore habitat selection, although these two aspects of conflict rarely have been examined concurrently. Advances in animal tracking have facilitated examination of carnivore habitat selection and movements affording new opportunities to understand spatial patterns of conflict. We reviewed 10 years of data on conflicts between grizzly bears and humans in southwestern Alberta, Canada. We used logistic regression models in a geographic information system to map the probability of bear–human conflict from these data, and the relative probability of grizzly bear habitat selection based on global positioning system radiotelemetry data. We overlaid these maps to identify ecological traps, as well as areas of secure habitat. The majority of the landscape was seldom selected by bears, followed by ecological traps where most conflicts occurred. Only a small portion of the landscape was identified as secure habitat. Such mapping methods can be used to identify areas where conflict reduction strategies have the greatest potential to be effective. Our results highlight the need for comprehensive management to reduce conflicts and to identify areas where those conflicts are most problematic. These methods will be particularly useful for carnivores known to be in conflict with agriculture, such as large carnivores that prey on livestock, or pose a threat to human safety.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".