The trade of Saiga Antelope horn for traditional medicine in Thailand
Bibliographic record
Abstract
Demand for Saiga Antelope Saiga tatarica horn products in Southeast Asia, due to their perceived medicinal value, has drastically impacted the conservation of this species. At the same time, poor understanding of the dynamics of this trade in parts of Southeast Asia continues to impede regulation and conservation efforts. Here we examine the trade of Saiga horn products in Thailand through a rapid physical and online market survey, and via an analysis of Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) trade data. We found an active local trade in Saiga horn products in Thailand, with both physical market surveys and online surveys showing predominantly two forms of Saiga horn products in the market, i.e., cooling water and horn shavings (mostly sold as pre-packaged boiling kits). These products are commercially marketed as staple household medicines. Greater scrutiny, monitoring and research is urgently needed to understand how the use of Saiga horn is being regulated in Thailand including the number of licensed traders, potential stockpiles and management of these. Traditional medicine outlets and online sales of commercial Saiga horn products also requires attention. As a non-native species, the Saiga Antelope is not protected in Thailand which makes it difficult for enforcement authorities to prevent illegal trade of Saiga horn products within the country. Thailand is currently revising its wildlife laws with the intention of addressing the protection of non-native and CITES-listed species. Considering the widespread use of Saiga horn in Thailand, we recommend that Saiga Antelope be included in the revised species protection lists to enable enforcement action against trade in illegally sourced Saiga horn products.
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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 | 0.001 | 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) | 0.001 | 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 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".