Seahorses helped drive creation of marine protected areas, so what did these protected areas do for the seahorses?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
SUMMARY In marine environments, charismatic or economically valued taxa have been used as flagships to garner local support or international funds for the establishment and management of marine protected areas (MPAs). Seahorses ( Hippocampus spp.) are frequently used as flagship species to help engender support for the creation of small community-managed no-take MPAs in the central Philippines. It is thus vital to determine whether such MPAs actually have an effect on seahorse abundance, reproductive status and size. A survey of seahorses inside and immediately adjacent to eight MPAs, and in four distant unprotected fishing areas, showed these MPAs had no significant effect on seahorse densities; although densities in and near MPAs were higher than in the distant fished sites, seahorse densities did not change over time. Seahorse size did show a marginal reserve effect, with slightly larger seahorses being found inside MPAs as compared to the distant unprotected fishing areas, but, in general, MPAs had little impact on seahorse size. Although MPAs may eliminate local fishing pressure, they may not reduce other threats such as pollution or destructive fishing outside the reserves. Other recovery tools, such as ecosystem-based management, habitat restoration and limits on destructive fishing outside of MPAs, may be necessary to rebuild seahorse populations. The effects of MPAs depend on species, as well as conditions outside the reserve boundaries. MPA management objectives must thus be clearly and realistically articulated to the communities, especially if support for an MPA was derived at least partly to conserve a particular flagship species.
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.
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 | 0.001 |
| 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 it