Use of Market Data to Assess Bushmeat Hunting Sustainability in Equatorial Guinea
Bibliographic record
Abstract
Finding an adequate measure of hunting sustainability for tropical forests has proved difficult. Many researchers have used urban bushmeat market surveys as indicators of hunting volumes and composition, but no analysis has been done of the reliability of market data in reflecting village offtake. We used data from urban markets and the villages that supply these markets to examine changes in the volume and composition of traded bushmeat between the village and the market (trade filters) in Equatorial Guinea. We collected data with market surveys and hunter offtake diaries. The trade filters varied depending on village remoteness and the monopoly power of traders. In a village with limited market access, species that maximized trader profits were most likely to be traded. In a village with greater market access, species for which hunters gained the greatest income per carcass were more likely to be traded. The probability of particular species being sold to market also depended on the capture method and season. Larger, more vulnerable species were more likely to be supplied from less-accessible catchments, whereas there was no effect of forest cover or human population density on probability of being sold. This suggests that the composition of bushmeat offtake in an area may be driven more by urban demand than the geographic characteristics of that area. In one market, traders may have reached the limit of their geographical exploitation range, and hunting pressure within that range may be increasing. Our results demonstrate that it is possible to model the trade filters that bias market data, which opens the way to developing more robust market-based sustainability indices for the bushmeat trade.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".