Moving marine aquaculture towards the Regenerative Blue Economy framework
Bibliographic record
Abstract
The target 3 of the Kunming-Montreal Global Biodiversity Framework emphasizes the need for innovative approaches in coastal areas to achieve marine ecosystem conservation and food security while promoting inclusivity for coastal populations. For the aquaculture sector, representing more than 50 percent of the seafood produced in the world (FAO, 2024), there is an urgent need to enhance the sustainability of marine aquaculture systems often seen as negatively impacting marine biodiversity and social tensions in coastal communities. This study proposes a transition towards ocean-positive aquaculture practices that not only minimize environmental impacts, but also support marine and coastal ecosystem functions and ensure equitable access to resources and benefits for local communities.The research examines the Regenerative Blue Economy (RBE) framework as a comprehensive approach to achieve these goals (Le Gouvello & Simard, 2024). The RBE principles are analyzed with related concepts which emphasize enhancing the resilience of food systems against environmental changes and supporting small-scale actors and coastal communities such as: restorative aquaculture (Alleway et al., 2023), regenerative aquaculture (Mizuta et al., 2023), ecological aquaculture, marine permaculture (Spillias et al., 2024), aquaculture as a Nature-based Solution (Le Gouvello et al., 2023) and a more inclusivity in the aquaculture sector (Brugere et al., 2023).The study identifies key indicators to guide aquaculture development policy and practices towards the RBE framework, focusing on improving the understanding of aquatic foods' roles in food security and nutrition, and addressing safety concerns related to ocean-based food systems. The relevance of this approach is demonstrated through a case study in a community-managed marine protected area in Senegal, where clam production initiatives empower women and enhance local food sovereignty.By fostering transgenerational access to aquatic foods and integrating Indigenous knowledge, this research contributes to the development of equitable and sustainable marine aquaculture practices. The findings highlight synergies with marine conservation tools, such as Marine Protected Areas and other effective conservation measures (Le Gouvello et al., 2017). Recommendations for policymakers are presented, emphasizing strategies to transition marine aquaculture systems towards a regenerative model that supports a sustainable future for ocean-based foods.Alleway, H. K., et al. (2023). doi.org/10.1111/csp2.12982Brugere, C., et al. (2023). doi.org/10.1111/jwas.12959FAO. (2024). doi.org/10.4060/cd0683enLe Gouvello, R., et al. (2023). doi.org/10.3389/fmars.2023.1146637Le Gouvello, R., et al. (2017). doi.org/10.1002/aqc.2821Le Gouvello, R., & Simard, F. (2024). https://portals.iucn.org/library/node/51442Mizuta, D. D., et al. (2023). doi.org/10.1111/raq.12706Spillias, S., et al. (2024). doi.org/10.1016/j.oneear.2024.01.012
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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 | 0.009 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".