Assessing seafood nutritional diversity together with climate impacts informs more comprehensive dietary advice
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.000 | 0.001 |
| 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 it