The illegal trade of binturongs in Indonesia (arctictis binturong)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 it