Linking catch reconstructions with downstream supply chain nodes can help strengthen management actions in favour of just, sustainable and resilient futures
Bibliographic record
Abstract
Supply chain opacity enables seafood fraud, human rights abuses and unsustainable resource use. To boost seafood transparency, it is vital to understand what is being caught or farmed, by whom, how, where and when. Catch reconstructions, such as those from the Sea Around Us, achieve this. Yet, linking producers’ outputs with downstream supply chain nodes (processors, distributors, wholesalers, retailers, or consumers) provides insights into who utilizes the production, its sourcing, and intended use. This paper describes a methodology for establishing links between marine taxa, producers, and downstream nodes over time. Peru is used as a case study to illustrate the approach. The resulting database covers the period between 1950 and 2021, and links 15 producers with 11 downstream nodes, across 45 functional groups (aggregating 340 taxa). A closer look at the data reveals that downstream nodes are growing increasingly reliant on inputs sourced from small-scale fisheries. Moreover, the use of unreported inputs by nodes involved in export-driven supply chains is declining, while the opposite trend is observed by nodes along supply-chains whose end consumers are domestic. Links between downstream nodes and producers are very variable and dynamic. Yet, all nodes show signs of environmental regime shifts, Fishing Down the Food Web and the expansion of fishers’ domains. The implications for supply chain transparency and fisheries systems research are discussed in detail.
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How this classification was reachedexpand
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".