Managing offshore multi-use settings: Use of conceptual mapping to reduce uncertainty of co-locating seaweed aquaculture and wind farms
Bibliographic record
Abstract
The offshore Multi-use Setting (MUS) is a concept that aims to co-locate marine industrial activities, including wind farms and aquaculture. MUS is considered an innovative approach to promoting efficiency in space and resource use whilst contributing global policy priorities. However, the impacts of MUS development across social, economic, and environmental domains are uncertain, hindering the commercialisation of the concept. In this study, we initially demonstrate the potential consequences of co-locating seaweed aquaculture and a wind farm as a step towards MUS. Using a hypothetical case study and modified Delphi methodology, 14 subject matter experts predicted potential outcomes across social and environmental objectives. Five Cognitive maps and impact tables of 58 potential consequences were generated based on experts' perspective on co-locating seaweed aquaculture and a wind farm. The findings highlight the potential to exasperate pressures in the area, including those already attributed to wind farm operations, such as species mortality and stakeholder conflict. However, it may also enhance social-ecological conditions, such as resource provisioning and promoting habitat functionality in the region, through the addition of seaweed aquaculture. The cognitive maps demonstrate the complexity of managing MUS implementation, where high degree of variability and uncertainty about the outcomes is present. The findings of this study provide the vital entry point to performing further integrative assessment and modelling approaches, such as probabilistic analysis and simulations, in support of MUS decision-making. The research also strongly recommends alternative strategies in the pursuit of combining seaweed production and wind farms to avoid significant financial (among many other) trade-offs and risks. More broadly, we have found that our approach's ability to visually represent a complex situation while considering multiple objectives could be immensely valuable for other bioeconomy innovations or nature-based solutions. It helps mitigate the potential for expensive investments without a comprehensive evaluation of the associated risks and negative impacts, as necessitated by the principles of sustainability in decision-making.
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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 | 0.017 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".