Bears in the Russian Far East illegally exploited for meat, medicine and trophies
Why this work is in the frame
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Bibliographic record
Abstract
Background and Research Aims Russia is a key source of bear parts in illegal trade but bear trade dynamics within the country is unknown. This study aims to address this gap by examining the legal and illegal international trade of bears in the Russian Far East. Methods Illegal trade of bears from the Russian Far East was analysed using seizure data from the Russian customs authorities from 2015 to 2019, while legal trade was analysed using CITES trade data. Results There were 116 seizures of bears involving the Russian Far East. Bear paws, claws and gall bladders were the main commodities seized revealing a demand for meat, trophies and medicine. During the same timeframe, Russia legally exported bear trophies, parts and derivatives to 55 countries and territories. Trophies were largely destined to the US and European countries whereas bear gall bladders, paws and derivates to Hong Kong. Conclusion This study shows that bears in Russia are threatened by poaching and illegal trade. They are killed for their gall bladders which are exported to Asian markets. They are also killed and exported as trophies predominantly to the US and European countries. As a game resource, Russia permits the hunting of Asiatic black bears and brown bears within established harvest quotas. Despite this, bears are being illegally killed and trafficked beyond Russia’s borders in violation of national laws and CITES trade regulations. Implications for Conservation Illegally sourced bear parts from Russia have been found in numerous countries across the globe. Further research is needed to quantify the overall illegal trade from Russia to understand the impact illegal offtake and trade has on wild bear populations in Russia. Further, the hunting of bears in Russia warrants greater regulation and monitoring to prevent the poaching of their parts for trade.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| 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