Assessing seafood nutritional diversity together with climate impacts informs more comprehensive dietary advice
Bibliographic record
Abstract
Abstract Seafood holds promise for helping meet nutritional needs at a low climate impact. Here, we assess the nutrient density and greenhouse gas emissions, weighted by production method, that result from fishing and farming of globally important species. The highest nutrient benefit at the lowest emissions is achieved by consuming wild-caught small pelagic and salmonid species, and farmed bivalves like mussels and oysters. Many but not all seafood species provide more nutrition at lower emissions than land animal proteins, especially red meat, but large differences exist, even within species groups and species, depending on production method. Which nutrients contribute to nutrient density differs between seafoods, as do the nutrient needs of population groups within and between countries or regions. Based on the patterns found in nutritional attributes and climate impact, we recommend refocusing and tailoring production and consumption patterns towards species and production methods with improved nutrition and climate performance, taking into account specific nutritional needs and emission reduction goals.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".