Regional patterns of wild animal hunting in African tropical forests
Bibliographic record
Abstract
Abstract Wildlife contributes to the diets, livelihoods and socio-cultural activities of people worldwide; however, unsustainable hunting is a major pressure on wildlife. Regional assessments of the factors associated with hunting offtakes are needed to understand the scale and patterns of wildlife exploitation relevant for policy. We synthesized 83 studies across West and Central Africa to identify the factors associated with variation in offtake. Our models suggest that offtake per hunter per day is greater for hunters who sell a greater proportion of their offtake; among non-hunter-gatherers; and in areas that have better forest condition, are closer to protected areas and are less accessible from towns. We present evidence that trade and gun hunting have increased since 1991 and that areas more accessible from towns and with worse forest condition may be depleted of larger-bodied wildlife. Given the complex factors associated with regional hunting patterns, context-specific hunting management is key to achieving a sustainable future.
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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 | 0.001 |
| 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".