Opportunities and challenges for analysis of wildlife trade using CITES data – seahorses as a case study
Bibliographic record
Abstract
Abstract In principle, the database generated by the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) offers an unparalleled opportunity to analyse trade in species of conservation concern. The value of the database is assessed in the context of trade in 47 species of seahorse (Hippocampus spp.), all of which are included on CITES Appendix II. This listing requires that all 180 Parties to CITES (member Parties) limit exports to levels that do not damage wild populations, ensure they are obtained legally, and report their trade to CITES. An evident need for greater universal compliance with CITES reporting requirements was identified. The most glaring problem was a substantial mismatch in species and volumes between export records and import records, indicating that neither dataset is complete nor reliable. The evaluation also showed that Parties should increase compliance with CITES requirements to record all trade shipments, provide units for exports (e.g. individuals, kilograms) and identify exported taxa to species, perhaps supported by automated checking of entries. The challenges with the CITES trade database were more evident for the global trade in dried seahorses than the smaller and more easily‐tracked trade in live seahorses. Nonetheless, CITES’ data from 2004–2011 revealed a seahorse trade involving millions of animals, tens of species, and scores of Parties. CITES data have also proven invaluable in supporting CITES reviews of how Parties are implementing the Convention for seahorses, and in generating consequent action for their conservation. Copyright © 2014 John Wiley & Sons, Ltd.
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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 | 0.025 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.013 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".