Navigating sustainability and health trade-offs in global seafood systems
Bibliographic record
Abstract
Abstract Seafood is expected to play a key role in improving access to healthy diets while providing food products with relatively low rates of greenhouse gas emissions. However, both nutrients and carbon footprints vary among species and production methods, and seafood consumption is further influenced by price and consumer preference, such that it is unclear which species are best placed to provide low-emissions nutritious seafood. Here, we use seafood production data to assess the nutritional value, carbon emissions, sustainability, affordability, and availability of seafood available to UK consumers. Globally, most seafood products are more nutritious and emit lower greenhouse gases than terrestrial animal-source foods, particularly small pelagic fishes and bivalves that contributed to recommended intakes for 3–4 essential dietary nutrients at the lowest emissions. For seafood products relevant to UK markets and consumers, Atlantic mackerel had the highest availability (i.e. landings) of all wild-caught UK seafood and lowest carbon footprint of all finfish, with one fillet portion exceeding recommended intakes of three nutrients (selenium, vitamins B12 and D). We found that price and sustainability of UK seafood, both factors in consumer demand, had considerable trade-offs with nutrients, carbon footprint, and availability. Farmed salmon, for example, were produced in large volumes but were relatively more expensive than other seafood, whereas highly nutritious, low-emissions farmed mussels had limited production volumes. The UK’s seafood system is therefore not currently optimised to produce nutritious, low-emissions seafood in large amounts. Policies that promote local consumption of affordable species already produced in high volumes, such as mackerel, could improve intakes of nutrients that are deficient in the UK population at relatively low environmental cost.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".