Illegal Harvesting within a Protected Area: Spatial Distribution of Activities, Social Drivers of Wild Meat Consumption, and Wildlife Conservation
Bibliographic record
Abstract
The African tropical forests host an inestimable number of resources, including food, medicine, vegetal and animal species. Among them, chimpanzees are threatened with extinction by human activities affecting their habitats, such as forest product harvesting, and/or more directly, snaring and trafficking. We aimed to better understand the spatial distribution of these illegal activities, and the reasons for setting snares and consuming wild meat in an agricultural landscape (subsistence farming and cash crops) densely populated near a protected area (Sebitoli, Northern part of Kibale National Park, Uganda). To carry out this study, we combined GPS records of illegal activities collected with group counts (in total, n = 339 tea workers, 678 villagers, and 1885 children) and individual interviews (n = 74 tea workers, 42 villagers, and 35 children). A quarter of illegal activities collected (n = 1661) targeted animal resources and about 60% were recorded in specific areas (southwest and northeast) of the Sebitoli chimpanzee home range. Wild meat consumption, which is illegal in Uganda, is a relatively common practice among participants (17.1% to 54.1% of respondents depending on actor types and census methods). However, consumers declared that they eat wild meat unfrequently (0.6 to 2.8 times per year). Being a young man coming from districts contiguous to Kibale National Park particularly raises the odds of consuming wild meat. Such an analysis contributes to the understanding of wild meat hunting among traditional rural and agricultural societies from East Africa.
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How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".