Magnitude and inferred impacts of the seahorse trade in Latin America
Bibliographic record
Abstract
Seahorses (genus Hippocampus) are traded globally for use in traditional medicines, souvenirs and as aquarium fishes. Indications that the trade was expanding geographically in response to increasing demand in consuming nations prompted this first study of the seahorse trade in Latin America. In 2000, over 400 people related to the seahorse trade in Mexico, Central America, Ecuador and Peru were interviewed. Customs data and other trade records from these and five additional countries or regions trading seahorses from Latin America were obtained. Dried seahorses were exported by almost every surveyed country at some point in the 1990s, with Ecuador, Peru and Mexico exporting hundreds of kg per year over multiple years, and the latter two nations both exporting tonnes of seahorses at least twice. The live seahorse trade was confined to Costa Rica, Mexico, Panama and Brazil; the last dominating this trade and exporting several thousand seahorses annually. Substantial declines in seahorse abundance, attributed primarily to incidental catches in shrimp trawl fisheries, were reported consistently by respondents in many regions. These data contributed to an Appendix II listing on the Convention on International Trade in Endangered Species of Wild Fauna and Flora of all seahorses, thereby requiring that the trade be monitored and controlled. Additional conservation measures are needed to address fishing pressure on seahorse populations.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".