The global footprint of drifting Fish Aggregating Devices
Bibliographic record
Abstract
Tuna are among the world’s most valuable marine life and have long been exploited by industrial fisheries. Increasingly, tuna fishing companies have shifted from targeting free-swimming fish to using drifting fish aggregating devices (dFADs): satellite-tracked rafts that move with currents while accumulating fish below. Here we estimate the global footprint of these devices and track 30 years of progress to mitigate impacts. We estimate 1.41 million dFAD buoys were released between 2007-2021, drifting across at least 134 million km2, or 37% of Earth’s ocean surface. Lost dFADs have stranded in 104 maritime regions, contributing to coastal pollution and damaging sensitive habitats. Regulatory progress has been made to address data quality, entanglement, and pollution, but concerns over unregulated dFAD deployments, unsustainable bycatch, and weak industry accountability persist. Our results demonstrate that the cumulative environmental footprint of dFADs reaches far beyond tuna fishing grounds and remains inadequately mitigated at the global scale.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".