A rapid assessment of the illegal otter trade in Vietnam
Bibliographic record
Abstract
Abstract Vietnam is home to four species of otters, and while population numbers are unknown, they are thought to be rare and in decline. Studies on the illegal otter trade in Asia have shown Vietnam to be a key end use destination for illegally sourced live otters for the pet trade and otter fur for the fashion industry. This study focused on the otter trade in Vietnam through seizure data analysis and an online survey, revealing the persistent trade of otters in Vietnam in violation of national wildlife laws and the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES). We found a substantial quantity of otter fur products for sale though CITES permits for such products were lacking, indicating illegal origins. Similarly, all four species of otters are protected in Vietnam, yet they were openly available for sale online in violation of national wildlife laws. Clearly, the online trade of wildlife and wildlife products in Vietnam requires greater monitoring, regulation, and enforcement to prevent the advertising and trade of illicit wildlife. In-depth scrutiny of online sellers and product sourcing is particularly warranted. To support enforcement efforts, revision of policies and laws is needed to hold social media and other online advertising companies accountable for enabling the illegal trade of wildlife.
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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 | 0.001 | 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 | 0.000 | 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) | 0.002 | 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".