Anticipating trade-offs and promoting synergies between small-scale fisheries and aquaculture to improve social, economic, and ecological outcomes
Bibliographic record
Abstract
Abstract Blue food systems are crucial for meeting global social and environmental goals. Both small-scale marine fisheries (SSFs) and aquaculture contribute to these goals, with SSFs supporting hundreds of millions of people and aquaculture currently expanding in the marine environment. Here we examine the interactions between SSFs and aquaculture, and the possible combined benefits and trade-offs of these interactions, along three pathways: (1) resource access and rights allocation; (2) markets and supply chains; and (3) exposure to and management of risks. Analysis of 46 diverse case studies showcase positive and negative interaction outcomes, often through competition for space or in the marketplace, which are context-dependent and determined by multiple factors, as further corroborated by qualitative modeling. Results of our mixed methods approach underscore the need to anticipate and manage interactions between SSFs and aquaculture deliberately to avoid negative socio-economic and environmental outcomes, promote synergies to enhance food production and other benefits, and ensure equitable benefit distribution.
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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 | 0.019 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".