Quantifying global redundant fisheries trade to streamline seafood supply chains
Bibliographic record
Abstract
Seafood plays an important role in sustainably feeding the world and is one of the most traded food products globally. However sustainability improvements are often focused on its production (e.g., aquaculture, fishing) rather than trade. Here, we quantify the magnitude and extent of global 'redundant two-way' seafood trade-the exchange of the same quantity of the same taxonomic species between two countries-to examine its prevalence and potential implications across the seafood supply chain. We focused on wild-caught seafood trade and found that redundant two-way trade has increased by 43%, between 2000 and 2015, making up 3.2% (7.7 Mt) of global seafood trade during that period. Although most countries were involved in redundant two-way seafood trade (111 of 212 analyzed), the majority occurred between five trade partners: Canada and the United States (15%), Germany and the Netherlands (11.8%); Denmark and Sweden (10.6%); Germany and Denmark (7.1%); and France and Norway (7%). Nearly 50% of redundant trade is made up of just four species including Atlantic herring, Atlantic cod, Skipjack tuna and Atlantic mackerel. While deficiencies in global seafood trade data mask seasonal and product heterogeneity, redundant trade could have implications for meeting conservation and sustainable development goals. Future research should build upon these findings to explore specific environmental, economic, and social implications associated with redundant two-way trade to benefit producers and consumers within the seafood supply chain.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".