Drifting fish aggregating devices in the Indian ocean impacts, management, and policy implications
Bibliographic record
Abstract
The Indian Ocean has seen a rise in technologically advanced drifting fish aggregating devices (dFADs), significantly increasing tropical tuna catches. These devices, equipped with GPS buoys and echo sounders, enhance fishing efficiency but also lead to increased juvenile tuna and bycatch species catches, ghost fishing, and abandoned gear. This study assesses the technological sophistication, and ecological impacts of dFADs in the region, particularly their role in IUU fishing when they drift into the Somali EEZ. Over a six-month period, 80-dFADs were opportunistically recovered along the four-sample coastline, with 63 being included analysis. None of the recovered dFADs complied with IOTC regulations. The study estimated the potential number of dFADs per km per annum over the Somali shelf as 1395 dFADs that could theoretically be recovered annually. This underscores substantial regulatory non-compliance and emphasizes the need for enhanced monitoring, stricter regulations, and IOTC cooperation to address the ecological and economic impacts on regional marine ecosystems and communities.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".