Evaluating the relationships between the legal and illegal international wildlife trades
Bibliographic record
Abstract
Abstract The international legal trade in wildlife can provide economic and other benefits, but when unsustainable can be a driver of population declines. This impact is magnified by the additional burden of illegal trade, yet how it covaries with legal trade remains little explored. We combined law‐enforcement time‐series of seizures of wildlife goods imported into the United States (US) and the European Union (EU) with data on reported legal trade to evaluate the evidence for any relationships. Our analysis examined 28 US and 20 EU products derived from CITES‐listed species with high volume and frequency of both reported trade and seizures. On average, seizures added 28% and 9% to US and EU reported legal trade levels respectively, and in several cases exceeded legal imports. We detected a significant but weak overall positive relationship between seizure volumes and reported trade into the US over time, but not into the EU. These results highlight the importance of maintaining long‐term records of border seizures and enforcement effort, and accounting for illegal trade where possible in non‐detriment findings. Our findings suggest a complex and nuanced temporal association between the illegal and legal wildlife trades.
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How this classification was reachedexpand
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 | 0.000 |
| 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) | 0.001 | 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 teacher head, 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".