Assessing Human-Common Leopard Negative Interaction: Mitigating Poaching and Illegal Trading of its Products in Eastern Himalaya.
Bibliographic record
Abstract
The population of the Common Leopard (Panthera pardus) are increasingly becoming isolated due to human activities, which has increased human-leopard interaction. Bhutan is experiencing an increase in human-wildlife conflict, partly as the farmland and crops are being abandoned, which has allowed wildlife to encroach upon human settlements. It is extremely difficult to balance farmers’ socioeconomic requirements with ecological conservation. Large animals like tigers and snow leopards have been the subject of much investigation but the leopards have received less attention. To address this gap, a study was conducted to document incidents of livestock depredation by leopards, identify the threats they face, and raise awareness about leopard conservation. Before commencing the field survey, researchers engaged with villagers to gather general information about leopards and their habitats. Data collection involved active participation from the local community, with a total of 340 respondents, mostly comprising elderly villagers and pastoral nomads. Over the past five years, 242 livestock animals fell victim to predators, with the highest number of attacks occurring in 2018. The Common Leopard was responsible for the majority of livestock losses in the study area. Incursions by leopards into human settlements have been on the rise, exacerbating conflicts, especially among rural communities residing near the forest. Some villagers resorted to killing leopards as a form of retaliation against livestock losses and to trade their body parts for various purposes. To address these challenges, forest officials and managers received guidance on monitoring and leopard conservation. The involvement of local communities through extensive awareness campaigns played a crucial role in supporting leopard conservation efforts.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".