MétaCan
Menu
Back to cohort
Record W4406190645 · doi:10.1080/10871209.2024.2449420

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

2025· article· en· W4406190645 on OpenAlex

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.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueHuman Dimensions of Wildlife · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsnot available
FundersUniversity of British ColumbiaWildlife Conservation Society
KeywordsLeopardGeographyTree (set theory)Environmental resource managementEcologyBiologyEconomics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score0.533

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.017
GPT teacher head0.251
Teacher spread0.233 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it