Asiatic Black Bear–Human Conflict: A Case Study from Guthichaur Rural Municipality, Jumla, Nepal
Bibliographic record
Abstract
)-human conflicts within the Guthichaur rural municipality, Jumla, Nepal. Through semi-structured interviews with villagers, focus group discussions (FGDs), and key informant interviews (KIIs), we gathered black bear-human conflict information from 2009 to 2019. We identified three primary types of black bear-human interactions: crop damage, livestock depredation, and human injuries. Of these, crop damage (77.03%) emerged as the most prevalent issue. Notably, peak occurrences were observed during autumn (September-October) typically between 9 PM and 3 AM. Livestock depredations were more frequent during nighttime in April-August, with cows/ox (70.12%) being the most depredated animal. Our data also revealed five recorded cases of black bear attacks on humans, which transpired from September to October, primarily in farmland areas in varying years. Despite a prevailing negative perception of bears, a notable level of support exists for their conservation efforts among local communities. Furthermore, these conflicts could be mitigated by reinforcing indigenous crop protection methods and implementing targeted mitigation strategies, as observed in other regions with successful black bear-human interaction management.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".