Climate change exacerbates nutrient disparities from seafood
Bibliographic record
Abstract
Abstract Seafood is an important source of bioavailable micronutrients supporting human health, yet it is unclear how micronutrient production has changed in the past or how climate change will influence its availability. Here combining reconstructed fisheries databases and predictive models, we assess nutrient availability from fisheries and mariculture in the past and project their futures under climate change. Since the 1990s, availabilities of iron, calcium and omega-3 from seafood for direct human consumption have increased but stagnated for protein. Under climate change, nutrient availability is projected to decrease disproportionately in tropical low-income countries that are already highly dependent on seafood-derived nutrients. At 4 o C of warming, nutrient availability is projected to decline by ~30% by 2100 in low income countries, while at 1.5–2.0 o C warming, decreases are projected to be ~10%. We demonstrate the importance of effective mitigation to support nutritional security of vulnerable nations and global health equity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.003 | 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".