Navigating human-sloth bear encounters and attacks in Nepal’s unprotected forests
Bibliographic record
Abstract
Human-sloth bear conflict is a recurring issue in multi-use forest landscapes outside protected areas (PAs). In Nepal’s southern region, sloth bears are a major contributor to human-wildlife conflict, yet comprehensive information to inform conflict mitigation and ensure human safety remain limited. To address this gap, we collected questionnaire-based interview data on sloth bear encounters and attacks from 1990 to 2021 around the Trijuga forest, an important sloth bear habitat outside of Nepal’s PAs. Within this time period, 66 human-sloth bear encounters involving 69 human individuals were recorded, with an annual average of 2.06 (SD = 1.48) encounters and 1.75 (SD = 1.34) attacks. Encounters primarily involved working-age men (25–55 years old), whose primary occupation was farming and who frequented the forest daily. They typically occurred between 0900 and 1500, inside forests, and in habitats with poor visibility conditions. Fifty-six encounters resulted in attacks by bears 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 the identification of high-risk bear encounter zones through participatory mapping with active community involvement, promoting sustainable alternatives to forest dependence, and outreach programs for local communities to enhance effective human-sloth bear conflict management in Nepal’s unprotected forests.
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 | 0.001 | 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".