Navigating human-sloth bear encounters and attacks in Nepal’s unprotected forests
Bibliographic record
Abstract
Abstract Human-sloth bear conflict, generally in the form of human attacks, is common throughout most areas where sloth bears co-occur with humans. The situation is more prevalent in multi-use forest landscapes outside protected areas. Although sloth bears are a conflict-prone species in Nepal, there is a clear lack of systematic studies that can inform human safety and conflict mitigation. We used data from questionnaire-based interviews with conflict-affected people and witnesses to provide detailed information about human-sloth bear conflict (1990– 2021) in the Trijuga forest, an important sloth bear stronghold outside protected areas in Nepal. The data were analyzed using descriptive statistics, chi-square tests, and regression analysis. For the time period, 66 conflict incidents involving 69 human individuals were recorded, with an annual average of 2.06 (SD = 1.48) incidents and 1.75 (SD = 1.34) attacks. Conflicts primarily impacted working-age group (25–55 years old) men, whose primary occupation was farming and who frequented the forest regularly. They typically occurred between 0900 and 1500, inside forests, and in habitats associated with poor land cover visibility. Poor visibility was also a significant positive determinant of bear attacks on humans. Fifty-six conflict incidents resulted in attacks that injured 59 people, with a fatality rate of 8.47%. Victims of bear attacks frequently had serious injuries, especially to the head and neck areas of the body. Serious injuries were more likely to occur to lone individuals than to people who were in groups of two or more. We suggest identification of conflict-risk habitats through a participatory mapping approach and education programs for the local people for effective human-sloth bear conflict management in Nepal’s unprotected forests.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".