Common Sun Skink Eutropis multifasciata (Kuhl 1820) sold for Traditional Medicine in Indonesia and potential conservation implications.
Bibliographic record
Abstract
Reptiles are one of the most frequently encountered animal species in the trade for traditional medicine. The use of reptiles for medicinal purposes has been documented throughout the world, impacting dozens of species. Despite the broad occurrence of reptiles in medicinal trade, there is a general lack of information concerning the scale or impact of this trade and the species involved. Here we report the sale of Common Sun Skinks Eutropis multifasciata on the island of Java in Indonesia. We surveyed 13 wildlife markets and three reptile pet stores in eight cities across Java, documenting 110 Common Sun Skinks in trade in six markets in five cities. This skink is sold for traditional medicinal purposes with several vendors stating its use to treat skin problems like “itchiness”. This particular use has not been well documented in Indonesia. Further, the Common Sun Skinks were primarily sold by vendors selling Tokay Geckos for a similar purpose. This is of potential conservation concern as Tokay Geckos are harvested en masse for medicinal purposes which has resulted in significant population declines, and this could lead to potential over¬exploitation of the Common Sun Skink in response to this decline.
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 | 0.000 | 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 | 0.001 | 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) | 0.003 | 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".