Comparing interview and trade data in assessing changes in the seahorse<i>Hippocampus</i>spp. trade following CITES listing
Bibliographic record
Abstract
Abstract Concerns regarding the sustainability of the seahorse Hippocampus spp. trade led to their listing on CITES Appendix II in 2002, with implementation in 2004. In 2007 we interviewed wholesale traders of seahorses in Hong Kong, China, seeking indications of the effects of the CITES listing on the seahorse trade. We cross-validated traders’ perspectives with government trade statistics (1998–2007) from Hong Kong and Taiwan. We also compared these data with trade statistics for pipefish, which are related species with similar medicinal uses but are not CITES-listed. Both the interviews and government statistics indicated reduced volumes of seahorses traded through Hong Kong, changes in source countries, and price increases post-implementation. Traders suggested that these changes were largely a result of the CITES listing. However, data indicate that other factors such as shifts in domestic policies and local demand may also have affected the trade. By cross-validating the perspectives of local stakeholders with trade statistics in a wildlife trading hub we were able to explore hypotheses on the local and global impacts of CITES. Such approaches are especially important for CITES-listed species because often there is no single data source that is complete and wholly reliable.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".