Claws in the Capital: Human–Leopard Conflict Hotspots and Community Perceptions in Kathmandu Valley, Nepal
Bibliographic record
Abstract
ABSTRACT In areas where forests and human‐dominated landscapes intersect, humans and wildlife compete or collide in their efforts to utilize natural resources. The Kathmandu Valley hosts a suitable habitat for several wildlife species, including the leopard ( Panthera pardus ), which inhabits the surrounding forest patches. Over the past few years, there has been an increase in human–leopard interactions that have raised concern for the increase in human–leopard conflicts. To better understand and visualize the conflict within the valley, this study modeled human–leopard conflict using leopard conflict data and environmental variables influencing those conflicts. A human–leopard conflict hotspot map was generated using the MaxEnt modeling approach, identifying the high‐risk zones primarily near forest edges and expanding settlements. The analysis highlights key factors, such as canopy cover, the human influence index, slope, and proximity to water bodies, influencing human–leopard conflict. Additionally, ordinal logistic regression was used to understand people's attitudes towards leopards, which remained largely positive despite rising conflicts between humans and leopards. The results showed an encouraging sign for governmental bodies seeking to mitigate conflicts through targeted, individual‐level awareness programs in the near future.
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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 | 0.001 |
| 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".