Fishing through the cracks: The unregulated nature of global squid fisheries
Bibliographic record
Abstract
While most research has focused on the legality of global industrial fishing, unregulated fishing has largely escaped scrutiny. Here, we evaluate the unregulated nature of global squid fisheries using AIS data and nighttime imagery of the globalized fleet of light-luring squid vessels. We find that this fishery is extensive, fishing 149,000 to 251,000 vessel days annually, and that effort increased 68% over the study period 2017-2020. Most vessels are highly mobile and fish in multiple regions, largely (86%) in unregulated areas. While scientists and policymakers express concerns over the declining abundance of squid stocks globally and regionally, we find a net increase in vessels fishing squid globally and spatial expansion of effort to novel areas. Since fishing effort is static in areas with increasing management, and rising in unmanaged areas, we suggest actors may take advantage of fragmented regulations to maximize resource extraction. Our findings highlight a profitable, but largely unregulated fishery, with strong potential for improved management.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| 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".