Who owns reefer vessels? Uncovering the ecosystem of transshipment in fisheries
Bibliographic record
Abstract
A central barrier to effective governance and accountability in fisheries is the limited transparency of corporate ownership. Transshipment-the transfer of catches, fuel, parts, or crew between fishing and cargo vessels known as reefers-is often criticized for its opacity and poor governance. Better insight into the beneficial ownership of vessels involved in transshipment and their operational patterns could lead to more effective management. Our study presents a publicly accessible database of reefers' owners, operators, and flags. We identified 569 individual reefers and found that Russian and Chinese owners control 26 and 20% of the global reefer fleet, respectively. Results also show that 65% of all reefer vessels fly the flags of Russia, Panama, or China. This high level of consolidation suggests considerable leverage for enhancing transparency and governance. Our findings highlight the potential for reforming existing transshipment practices through collaboration among owners, flag states, fishery regulators, and scientists.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".