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Record W4366771250 · doi:10.1111/aje.13151

<scp>Human‐wildlife</scp> conflicts in communities bordering a <scp>Savannah‐Fenced</scp> wildlife conservancy

2023· article· en· W4366771250 on OpenAlexaff
Marc Dupuis‐Désormeaux, Timothy N. Kaaria, John Kinoti, Adrian L. D. Paul, Saibala Gilisho, Francis Kobia, Reagan Onyango, Geoffrey Chege, David Kimiti, Mary Mwololo, Zeke Davidson, Suzanne E. MacDonald

Bibliographic record

VenueAfrican Journal of Ecology · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsYork University
Fundersnot available
KeywordsFencingWildlifeFence (mathematics)GeographyLivestockPopulationHabitatNational parkPastoralismAgroforestryEcologyBiologyForestryEngineeringArchaeologyEnvironmental health

Abstract

fetched live from OpenAlex

Abstract We discuss various human‐wildlife conflicts (HWC) inherent within communities bordering a mid‐sized, semi‐porous wildlife conservancy in Kenya. HWC are a growing issue as human population expands into wildlife habitat to put people and wildlife in more frequent contact and compete for scarce resources. In 2018, we surveyed the crop‐raiding and livestock depredation experiences of 918 households from 10 separate villages and asked about the experiences of the villagers with HWC over the past 3 years. These communities are protected from wildlife with two different fence designs, a standard 12‐strand electrical fence, and an upgraded predator‐proof fence design. We found that between 70% and 91% of respondents had experienced some form of HWC including 39.5% who reported threats to their person from wildlife encroachments despite electrical perimeter fencing. HWC happened more often at night and during the dry seasons. The most common encroachments were from elephants, hyenas, leopards, and baboons. Community respondents rated that the upgraded predator‐proof fences performed better than the standard 12‐strand fences. However, even the predator‐proof design had issues with keeping monkeys from entering the communities and crop raiding. We discuss potential mitigation measures, including an improved predator‐proof fencing design that incorporates butterfly stingers that may offer better protection.

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.

How this classification was reachedexpand

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.028
GPT teacher head0.255
Teacher spread0.227 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations10
Published2023
Admission routes1
Has abstractyes

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