Bushmeat and food security in the Congo Basin: linkages between wildlife and people's future
Bibliographic record
Abstract
Tropical moist forests in Africa are concentrated in the Congo Basin. A variety of animals in these forests, in particular mammals, are hunted for their meat, termed bushmeat. This paper investigates current and future trends of bushmeat protein, and non-bushmeat protein supply, for inhabitants of the main Congo Basin countries. Since most bushmeat is derived from forest mammals, published extraction (E) and production (P) estimates of mammal populations were used to calculate the per person protein supplied by these. Current bushmeat protein supply may range from 30 g person−1 day−1 in the Democratic Republic of Congo, to 180 g person−1 day−1 in Gabon. Future bushmeat protein supplies were predicted for the next 50 years by employing current E:P ratios, and controlling for known deforestation and population growth rates. At current exploitation rates, bushmeat protein supply would drop 81% in all countries in less than 50 years; only three countries would be able to maintain a protein supply above the recommended daily requirement of 52 g person−1 day−1. However, if bushmeat harvests were reduced to a sustainable level, all countries except Gabon would be dramatically affected by the loss of wild protein supply. The dependence on bushmeat protein is emphasized by the fact that four out of the five countries studied do not produce sufficient amounts of non-bushmeat protein to feed their populations. These findings imply that a significant number of forest mammals could become extinct relatively soon, and that protein malnutrition is likely to increase dramatically if food security in the region is not promptly resolved.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".