Marine mammals used as bait for improvised fish aggregating devices in marine waters of Ecuador, eastern tropical Pacific
Bibliographic record
Abstract
Fish aggregating devices (FADs) are floating objects typically used to attract and capture pelagic fish in industrial tuna fisheries. This study documents 9 cases, involving 31 marine mammals, of incidentally captured, killed or otherwise retrieved cetaceans and pinnipeds which were used, or presumably used, as bait for improvised fish aggregation devices (IFAD) by artisanal fishers in coastal Ecuador. At least 3 species of small cetaceans were affected, including pantropical spotted dolphinStenella attenuata, short-finned pilot whaleGlobicephala macrorhynchus, pygmy killer whaleFeresa attenuataand an unidentified small delphinid, as well as South American sea lionsOtaria byroniawhich were reportedly killed on purpose for this fishing practice. A sperm whalePhyseter macrocephalusand a humpback whaleMegaptera novaeangliaewere presumably found floating at sea and opportunistically exploited as FADs. The South American sea lion represented 80.6% of marine mammals used as bait associated with FADs (25 sea lions out of 31 marine mammals), while the remaining 5 (possibly 6) cetacean species represented 19.4%. This is the first report of baited FADs in Ecuador, the extent of which is still unknown. This fishing technique has not been documented in other nations along the west coast of South America, although baiting of gillnets with marine mammal parts is common in Peru. Without fisheries management and regulation, this illegal fishing practice could rapidly expand and lead to further direct kills and conservation problems for targeted marine mammal populations in the eastern tropical Pacific. A bottom-up fisheries policy in concert with community-based conservation to ban the use of marine mammals as FAD bait is recommended.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".