The Catch and Trade of Seahorses in the Philippines Post-CITES (2019)
Bibliographic record
Abstract
The catch and trade of seahorses (Hippocampus spp.) has been illegal in the Philippines since 2002, but the revision of the Philippines' domestic Fisheries Code in 2015 opened an opportunity to legalize seahorse fisheries and exports if they could be managed for sustainability. To generate vital knowledge in support of this option, we conducted 268 interviews with fishers and traders across seventeen coastal provinces in 2019. We observed a total median annual catch of ~1.5 to 1.6 million individual seahorses, with the tally depending on the method used. Fishers reported catching seahorses from ten different types of fishing gears. The gear with the highest CPUE was a modified push-net, which is pulled across the ocean floor (locally named a “micro-trawl”), with 100 seahorses caught gear-1day-1, while compressor divers contributed half the total estimated catch. Other important gears were spear/skin divers and standard push nets. The provinces of Iloilo, Masbate, Sulu, Bohol, and Palawan together accounted for over 80% of the total national catch estimate. We photographed little evidence of live trade or domestic use, suggesting that most captured seahorses were exported dried. Buyers reported selling seahorses for between three and five times the price they paid fishers. Of conservation concern, nearly all (98%) fishers reported a decline in seahorse catch over time and highly skewed sex ratios across all species. Our data will help the Philippines’ management agencies decide whether to support the re-opening of legal trade and, if so, how to make it sustainable.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".