Navigating human-sloth bear encounters and attacks in Nepal’s unprotected forests
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it