Assessing and implementing the concept of Blue Economy in Laurentian Great Lakes fisheries: Lessons from coupled human and natural systems
Bibliographic record
Abstract
Abstract Inland fisheries often receive little to no attention in global discussions about sustainable development. The consequences of overlooking inland fisheries in sustainability dialogues are increasingly problematic as fisheries stressors (e.g. overharvest, species invasion, climate change, habitat modification) intensify. Elevating the global profile of inland fisheries requires an approach for quantifying and clearly conveying the ecological, economic, and societal values of these systems. One such approach involves the Blue Economy, a multifaceted concept initially used to describe the intersection of marine conservation and sustainable use of marine resources for economic growth. Although conceptually powerful, the Blue Economy has rarely been applied to inland waters and fisheries. To address this knowledge gap, we conceptualized Laurentian Great Lakes fisheries from a Blue Economy perspective. In particular, we evaluated the utility of the coupled human and natural systems framework for characterizing the ecological, economic, and societal values of Laurentian Great Lakes fisheries and associated contributions to the Blue Economy (e.g. human livelihoods, food security, recreation, conservation, economic prosperity). There are numerous opportunities to leverage Coupled Human and Natural Systems methods (e.g. metacoupling, telecoupling) and associated mathematical models to advance fisheries science, inform fisheries management, and ultimately move toward a Blue Economy in the Laurentian Great Lakes. To that end, we demonstrated applications of these methods, discussed strategies for communicating with stakeholders, and provided insights for navigating challenges to developing a Blue Economy in the Laurentian Great Lakes—a model that could be used in the African Great Lakes and other large ecosystems in the world.
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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 | 0.000 | 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".