Static and Dynamic Social Network Models for the Analysis of Transshipment in Illegal Fishing
Bibliographic record
Abstract
Illegal fishing is a global problem affecting society in numerous ways: from impacting the sustainability of artisanal and subsistence fishing communities to disturbing the balance of delicate oceanic ecosystems through the systematic overexploitation of fish stocks. On a global level, the success criminal organizations have had with hiding their activities is largely attributed to the practice of transshipment (where a fishing vessel offloads its catch to a transport vessel at sea), which makes tracing the origins of illegally caught fish extremely difficult. Extensive research has been conducted to leverage data from the Automatic Identification System (AIS) to identify vessels fishing illegally, but current methods often overlook interactions between vessels and have not been shown to affect the global extent of illegal fishing. In this paper, transshipment encounters are modelled as a social network graph where nodes represent vessels and edges represent transshipment encounters between them. A static analysis of the transshipment networks is conducted to identify key vessels enabling illegal fishing through transshipment. A dynamic visualization tool is then implemented to provide context on the formation and evolution of transshipment networks over time. Based on the dynamic analysis of the network, insights are gained about different ways in which transshipment encounters might be used to offload marine products. A dynamic analysis of criminal subnetworks is conducted, showcasing how network modelling can be used to enable a global approach in the combat against illegal fishing.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".