Assessing the potential for seaweed aquaculture in Nova Scotia
Bibliographic record
Abstract
Growing interest in the seaweed aquaculture industry has focused on the environmental, economic and social benefits it can offer. Outside of Asia, it is a small but emerging industry with the potential to grow and contribute to food security, climate change mitigation and coastal economic development. However, limited understanding of this potential has led to slow and fragmented development of the industry, without a clear direction of how to move the industry forward. This research uses Nova Scotia, Canada as a case study to understand the potential for the seaweed aquaculture industry by analyzing the perceptions of stakeholder groups (industry, academia, NGO/community and government). A SWOT analysis was completed to understand drivers and barriers impacting the industry and was used to develop a Q-methodology survey for identifying important factors to consider in decision-making, management and planning of the industry. Results indicated that participants generally reflected one of two perspectives: the seaweed skeptic and the seaweed solutionist. Participant perceptions indicated areas where seaweed aquaculture can be a contributor in Nova Scotia, specifically in coastal community economic development and food sustainability. However, experiential knowledge gaps, uncertainties surrounding climate change impacts and lack of regulations appear to constrain individuals from fully supporting the industry. Further discussion is needed on the stewardship of and priorities for how this industry should be developed moving forward. These findings illustrate possible enabling conditions for the future of this industry in Canada.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".