Insights into the primate trade into the European Union and the United Kingdom
Bibliographic record
Abstract
Illegal and/or unsustainable trade is a major obstacle to effective primate conservation. The wildlife trade in the European Union (EU) and the United Kingdom (UK) is significant, but for many species, such as primates, the trade is poorly understood and sparsely reported. All EU countries are Party to the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES); all primates are listed on Appendix I or II of CITES and are included on Annex A or B of Regulation (EC) No 338/97. We here combine data from several databases (CITES, UN Comtrade, TRAFFIC WiTIS) and seizure reports, to provide a narrative of the trade in primates into and within Europe. The legal import of live primates (2002-2021) amounted to 218,000-238,000 individuals (valued at US$ 869 million), with France, the UK, and Spain as the main importers and Mauritius, Vietnam, and China as the main exporters. Over 21,000 primate parts (trophies, skulls, bodies) were imported mainly from African countries, and UN Comtrade data suggests that ~ 600 tonnes of primate meat was imported mainly from Asia. The vast majority of live primates are either captive-born or captive-bred, and this proportion has increased over time. Reports of the illegal primate trade are far from complete, but the illegal trade of specific species or primate meat can have negative impacts of wild populations of already imperiled species. Stronger policies and more effective enforcement in consumer countries, such as the EU, would also aid in, and garner support for, better protecting primates in primate range states.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.013 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".