The Efficiency of Motorcycle Use in Illegal Bushmeat Transportation in Western Serengeti, Tanzania
Bibliographic record
Abstract
Bushmeat is an important source of protein, as well as economic income for communities in sub-Saharan Africa and Latin America. This study was conducted in north-western Serengeti, Tanzania, from July to September of 2019. Two villages were sampled for distance calculation: Kowak and Robanda. The snowballing technique was the sampling design adopted. Trained assistants identified at least one bushmeat vendor in each village to be interviewed, who was thereafter asked to identify another vendor known to him/her. The number of days spent delivering bushmeat packages to the illegal market (an average of 200 km) from the bushmeat source was 16.8 days when using donkeys, 6.8 days when using bicycles, and 2.0 days when using motorcycles. Motorcycles were 8.4 and 3.4 times more efficient than donkeys and bicycles, respectively. Bicycles were 2.5 times more efficient than donkeys. The mean weights of bushmeat packages delivered by donkeys were 188.4 kg and 109.0 kg when using bicycles. Motorcycles delivered 185.0 kg of bushmeat per trip. The mean weights carried by donkeys and motorcycles were 1.7 times higher than those of bicycles. The mean depletion rates of motorcycles were 92.5 kg of bushmeat per day for a distance of 200 km. Bicycles depleted 16.0 kg, while donkeys only depleted 11.2 kg per day to the market. The use of motorcycles in bushmeat transportation increased the efficiency in delivering illegal bushmeat to predetermined illegal markets, and thus resulted in a high depletion rate. Wildlife authorities should introduce patrol systems that include the control of motorcycles close to protected areas. There should be day and night checkpoints in various places, such as large bridges that cannot be avoided and along rural pathways.
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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".