Human Use-Pressure and Sustainable Wildlife Management in Burkina Faso: A Case Study of Bushmeat Hunting in Bobo-Dioulasso
Bibliographic record
Abstract
Hunting is an important activity for the survival of local communities. However, unregulated hunting threatens the sustainability of wildlife and subsequently affects the same populations. This study investigated bushmeat hunting practices and their implications in wildlife sustainable management in Bobo-Dioulasso (Burkina-Faso). A total of 226 hunters were interviewed, using a random sampling technique and a semi-structured questionnaire. It revealed four groups of hunters. Group 1 (32.57% of the sample) was young and commercial hunters from Bobo ethnic group with 42.15±6.01 as average age. Hunting is their main activity and they hunt all year round in groups using direct catch and hunting dogs. Group 2 (19.76%) prefers to hunt in the daytime and their products serve for diseases treatments through traditional medicine. Group 3 (29.06%) consists of the Mossi ethnic group with an average age of 58.92±3.69. They belong to the confederation of hunters called "Dozo". They are farmers with hunting as the secondary activity. They hunt at night with headlamps. Group 4 (18.60%), mainly Mossi with an average age of 63.06±7.19, hunts occasionally and respects the accredited hunting periods. The animals at the risk and most commonly used as bushmeat are Francolin, Porcupine, Cape hare, Buffalo, Nile monitor, Python, and Parrot. The locally threatened animals are respectively Ostrich, Roan antelope, Bat, Crocodile, and Striped hyena. Other animals are endangered and becoming increasingly rare (Lion, Elephant, Hippopotamus, and Warthog). Actions need to be taken by decision-makers and involve local communities for the sustainable management of wildlife in Bobo-Dioulasso.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".