Achieving the 30x30 target in Seychelles using comprehensive marine spatial planning
Bibliographic record
Abstract
The Seychelles is an archipelagic nation in the Western Indian Ocean encompassing 1.35 million square kilometers of ocean and 115 islands. A large ocean state, marine biodiversity is one of Seychelles’ most important natural assets, supporting their Blue Economy. The Seychelles Marine Spatial Plan (SMSP) Initiative started in 2014 and is focused on planning for, and management of, the sustainable and long-term use and health of the Seychelles’ entire ocean. The SMSP is a necessary output of the government-led ocean debt conversion, a partnership with The Nature Conservancy that supports the Republic of Seychelles for marine conservation and climate change adaptation. Using a transparent, evidence-based and participatory MSP process, marine protection was expanded from 0.04% to more than 30% of Seychelles’ oceans by 2020. Marine zoning was developed in consultation with more than 12 marine sectors and civil society organisations. Guiding principles in the design of protection and multiple use zones reflect global best practices and the importance of effective implementation including that the comprehensive MSP is feasible, practical, equitable, and affordable. A legally enforceable Marine Spatial Plan will be completed and implemented in 2025. A phased approach for implementation will allow for the development and approval of management plans, operationalise the SMSP implementation authority, and other key steps for effective implementation of all marine protection areas and marine zones.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".