The illegal trade of binturongs in Indonesia (arctictis binturong)
Bibliographic record
Abstract
Wildlife trade heavily exploits small carnivores like the Binturong Arctictis binturong which is coveted for meat, skin, civet coffee production and the pet trade, across its range in Asia. Yet, there are few studies documenting the trade of binturongs or the impact of trade on wild populations. This study examines seizure data and online trade of binturongs in Indonesia to better understand trade dynamics and identify measures to mitigate illegal trade and exploitation. We found a significant quantity of binturongs for sale online with 594 adverts offering over 720 live animals during the study period, the majority of which were on Facebook (97.6%). The trade largely revolves around the demand for pets. Both wild-sourced and captive-bred individuals were observed for sale. Nevertheless, we argue the vast majority are likely to have been illegally harvested from the wild posing a serious threat to the survival of this unique small carnivore. This was supported by seizure data whereby 103 live binturongs were confiscated indicating illegal hunting for the species is occurring in violation of local wildlife laws. The vast number of adverts for binturongs indicates buyers and traders do not fear detection or perceive local enforcement as a threat. Addressing legislative weaknesses and greater enforcement of laws and prosecution rates will be essential in mitigating illegal trade and exploitation. Establishing clear and stringent regulations on online wildlife traders and platforms such as Facebook which facilitate this trade is urgently needed to end the rampant and blatant 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 | 0.000 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".