Charting risk pathways of leopard attacks on people: A decision tree approach
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
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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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 | 0.001 | 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