<scp>Human‐wildlife</scp> conflicts in communities bordering a <scp>Savannah‐Fenced</scp> wildlife conservancy
Bibliographic record
Abstract
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.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".