Assessing socio‐environmental suitability and social license of proposed offshore aquaculture development: A Florida case study
Bibliographic record
Abstract
Abstract Offshore aquaculture is a growing industry, but a lack of social acceptance is limiting development, including within the USA. We used the Gulf Coast of Florida, where there has been industry and government interest in development, as a case study to explore offshore aquaculture potential and methods for integrating stakeholder concerns into offshore aquaculture development. We assessed (1) social acceptance of offshore aquaculture in the Florida Gulf Coast using public comments; (2) site suitability for offshore development using social, biological, and technical data; and (3) potential impacts of offshore aquaculture on communities using socioeconomic vulnerability indices. We found that many stakeholders distrust policymakers and industry and have concerns about potential environmental impacts. We created species‐specific suitability maps for red drum ( Sciaenops ocellatus ) and almaco jack ( Seriola rivoliana ), demonstrating that large areas of the Gulf are suitable for offshore aquaculture development. We show that many coastal and fishing‐reliant communities have existing vulnerabilities that aquaculture development could affect, but the public comments did not reflect these. To gain social acceptance, industry and government agencies will need to better incorporate public feedback into planning processes in a meaningful way. Consulting local communities and adapting projects in response to their concerns can help to secure social license for offshore aquaculture.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".