Co-developing recommendations for biodiversity inclusive Marine Spatial Planning
Bibliographic record
Abstract
Achieving global commitments to protect and restore nature, such as the Kunming-Montreal Global Biodiversity Framework (GBF), is imperative and requires action at multiple scales. Countries have been required to align these goals within their respective policies to safeguard ecosystems, reduce pollution and ensure sustainable use of resources. For the marine realm, Marine Spatial Planning (MSP) offers a powerful tool to support biodiversity, combining science, stakeholders’ inputs and an ecosystem-based approach. An MSP process that is ‘biodiversity inclusive’ presents an opportunity to align human activities in a consistent manner to reduce pressures and preserve biodiversity.Recognizing the opportunity to reinforce biodiversity as key to ocean health and aiming to support countries developing and implementing participatory, integrated planning to avoid biodiversity loss (GBF target 1), UNESCO-IOC, the European Commission and UNEP partnered to co-develop recommendations on how to further include biodiversity considerations into MSP processes and plans. The recommendations were co-developed with practitioners and researchers from different parts of the word, building on their experience and expertise. These experts were brought together during online workshops to discuss the concept of biodiversity inclusive MSP, challenges and recommendations on how to advance it. Contributions were compiled, harmonized, revised and completed through an iterative process to develop actionable recommendations organized around the stages of the planning cycle to facilitate uptake. This process facilitates knowledge-transfer from different research projects, making it accessible to practitioners and policy makers. The publication develops the concept of biodiversity inclusive within the wider framework of the ecosystem-based approach that underpins MSP.
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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 | 0.058 | 0.174 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.018 | 0.015 |
| Insufficient payload (model declined to judge) | 0.025 | 0.017 |
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".