Trade and foreign fishing mediate global marine nutrient supply
Bibliographic record
Abstract
Fish are an important source of bioavailable micronutrients and essential fatty acids, and capture fisheries have potential to substantially reduce dietary deficiencies. Vigorous debate has focused on trade and fishing in foreign waters as drivers of inequitable distribution of volume and value of fish, but their impact on nutrient supplies from fish is unknown. We analyze global catch, trade, and nutrient composition data for marine fisheries to quantify distribution patterns among countries with differing prevalence of inadequate nutrient intake. We find foreign fishing relocates 1.5 times more nutrients than international trade in fish. Analysis of nutrient flows among countries of different levels of nutrient intake shows fishing in foreign waters predominantly (but not exclusively) benefits nutrient-secure nations, an outcome amplified by trade. Next, we developed a nutritional vulnerability framework that shows those small island developing states and/or African nations currently benefiting from trade and foreign fishing, and countries with low adaptive capacity, are most vulnerable to future changes in nutrient supplies. Climate change exacerbates vulnerabilities for many nations. Harnessing the potential of global fisheries to address dietary deficiencies will require greater attention to nutrition objectives in fisheries’ licensing deals and trade negotiations.
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.
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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 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".