Global stakeholder vision for ecosystem‐based marine aquaculture expansion from coastal to offshore areas
Bibliographic record
Abstract
Abstract Marine aquaculture is the most promising industry for ensuring future provision of seafood. Yet, the worldwide growth and expansion of this industry have been slower than expected, calling for the identification of environmentally suitable sites while accounting for all factors that could constrain or benefit its establishment. Here, we determine the main obstacles and risks hindering the growth and expansion of marine aquaculture, as well as the needs and recommendations to overcome such constraints. Our analysis is based on results obtained from a consultation process held in 16 study sites located around the world with the participation of 614 stakeholders representing the research community, aquaculture industry, government, conservation groups, and education and fishermen associations. A high level of commonality exists in the main issues hindering aquaculture growth and expansion in coastal, off‐the‐coast and offshore aquaculture with most being attributed to interactions with other maritime activities, including conflicts with other users and administrative procedures, including licensing. Critical needs for improved management and expansion of the aquaculture industry are related to planning and management of developments and technological advances, with economic and market needs featuring to a lesser extent. Key procedures recommended to assist further aquaculture growth are the standardisation and simplification of regulatory frameworks, improvement of governance, and the adoption of participatory processes to facilitate meaningful and productive stakeholder engagement. We strongly recommend stakeholder participation to enhance insights on the full environmental and human dimensions of marine management and for implementation of ecosystem‐based marine spatial planning.
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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 | 0.005 | 0.003 |
| 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 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".