Charting risk pathways of leopard attacks on people: A decision tree approach
Bibliographic record
Abstract
The often-under-researched aspect of human-wildlife conflict (HWC) is the socio-cultural factors affecting a community’s experience of HWC. In this study, we examine the risk of leopard attacks in North India where ~ 3 fatal leopard attacks occur on people per year. We used a mixed method approach to weigh the risks of a person experiencing a leopard attack in Himachal Pradesh (HP) across parallel scenarios by (a) calculating the most probable pathway of experiencing a high-impact (death/grievous injury) outcome due to leopard attacks (b) documenting perception of leopard attacks. In HP, 344 people experienced leopard attacks and most attacks (75%) were non-predatory. Few (12%) attacks on adolescents (<15 years) were predatory. We found mentions of intangible impacts in more than half of the interviews. This mixed method analysis, grounded on local voices of experience, could be utilized by researchers and managers to navigate complex scenarios in human-carnivore shared spaces.
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".