How Agencies Respond to Human–black Bear Conflicts: A Survey of Wildlife Agencies in North America
Bibliographic record
Abstract
Managing interactions between humans and American black bears (Ursus americanus) has evolved from public feeding and viewing of garbage-habituated bears to nationwide bear education campaigns focused on removing food attractants. We conducted a self-administered survey to assess how wildlife agencies respond to human–bear conflict and identified techniques currently used to manage conflicts throughout US, Canada, and Mexico. Forty-eight agencies responded to the survey and answered questions about bear populations, levels of complaints, types of interactions, and agency responses. Most (75%) agencies surveyed relocated problem bears, but only 15% believed relocation was an effective tool. Half (50%) of the agencies always marked problem bears that were captured and released; 50% both monitored the results of relocated bears and maintained a database. Most (69%) agencies ranked garbage/food attractants the most common type of human–bear conflict. Our results suggest that management responses to human–black bear conflict can be strengthened by adopting protocols for marking, monitoring, and maintaining a database for all bears captured in association with conflict incidents; moving from reactive to proactive approaches for garbage management; and developing comprehensive bear education programs that strive to make education a more dynamic and interactive process. Despite the unique circumstances of local politics and laws, all agencies need to strive to develop systems to document and evaluate the effectiveness of their actions to prevent and manage conflict. By monitoring actions and results, agencies can design improvements and move forward in an adaptive management framework.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".