DNA analysis of traded shark fins and mobulid gill plates reveals a high proportion of species of conservation concern
Bibliographic record
Abstract
Continuously increasing demand for plant and animal products causes unsustainable depletion of biological resources. It is estimated that one-quarter of sharks and rays are threatened worldwide and although the global fin trade is widely recognized as a major driver, demand for meat, liver oil, and gill plates also represents a significant threat. This study used DNA barcoding and 16 S rRNA sequencing as a method to identify shark and ray species from dried fins and gill plates, obtained in Canada, China, and Sri Lanka. 129 fins and gill plates were analysed and searches on BOLD produced matches to 20 species of sharks and five species of rays or - in two cases - to a species pair. Twelve of the species found are listed or have been approved for listing in 2017 in the appendices of the Convention on International Trade in Endangered Species of Fauna and Flora (CITES), including the whale shark (Rhincodon typus), which was surprisingly found among both shark fin and gill plate samples. More than half of identified species fall under the IUCN Red List categories 'Endangered' and 'Vulnerable', raising further concerns about the impacts of this trade on the sustainability of these low productivity species.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".