Applying genetic techniques to study remote shark fisheries in northeastern Madagascar
Bibliographic record
Abstract
BACKGROUND AND AIMS: The shark fisheries of Madagascar remain largely unstudied. Remoteness makes fisheries monitoring challenging while the high value of shark fins combined with the extreme poverty in Madagascar creates intensive pressure on shark resources. MATERIALS AND METHODS: We use DNA barcoding and species-specific PCR assays to characterize shark fisheries in Antongil Bay in northeastern Madagascar. RESULTS: The 239 samples taken from individuals collected in 2001 and 2002 correspond to 19 species. The four most common species were Sphyrna lewini, Rhizoprionodon acutus, Carcharhinus brevipinna, and C. sorrah. Antongil Bay may be a breeding area for C. brevipinna, C. leucas, and S. lewini. CONCLUSION: Local names are generally not a useful proxy for monitoring the species harvested in the fishery. Conservation efforts should characterize species exploitation at present, create spatial and temporal fishing restrictions to protect endangered species, and restrict large mesh gillnets.
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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 | 0.000 |
| 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".