Using online reports of seahorse seizures to track their illegal trade
Bibliographic record
Abstract
Illegal wildlife trade (IWT) is a persistent and extensive threat to global biodiversity. Hundreds of marine fish species are subject to regulation under the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES), but it is unclear how much protection species gain from CITES because information on marine fish IWT is limited. We used online reports of seized shipments of seahorses (Hippocampus spp.) to investigate their illegal trade. Seahorses were the first genus of marine fishes to be listed under CITES. We compiled 297 unique seizure records from 192 online outlets posted from January 2010 to April 2021 and analyzed the number of seahorses seized, the value of the seized items, trade routes, and other seizure details. Dried seahorses accounted for nearly all seizures, which totaled around 5 million individuals valued at over US$21 million. The reported number of seizures and the number of seahorses seized increased over time. Reported illegal trade involved 62 countries and other jurisdictions. Seized seahorses predominantly originated in Africa, Asia, and Latin America. China was the primary destination. Seahorses were mostly intercepted in transit and in destination countries rather than source countries. Airports were the most common location for seizures, and passenger baggage represented the primary transportation method by number of seizures, but sea cargo facilitated the largest seizures. Seahorses were most commonly seized by customs, often in conjunction with other regulated wildlife products. Although seizures led to detention of actors, information on subsequent legal actions was limited. Addressing the illegal trade in seahorses requires greater enforcement in source countries and increasing the realized risks associated with smuggling. Our findings can inform intelligence-led enforcement efforts to curb seahorse trafficking and highlight data biases and gaps that should be addressed to facilitate enhanced deterrence measures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 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".