DNA barcoding reveals the diversity of sharks in Guyana coastal markets
Bibliographic record
Abstract
ABSTRACT A fundamental challenge for both sustainable fisheries and biodiversity protection in the Neotropics is the accurate determination of species identity. The biodiversity of the coastal sharks of Guyana is poorly understood, but these species are subject to both artisanal fishing as well as harvesting by industrialized offshore fleets. To determine what species of sharks are frequently caught and consumed along the coastline of Guyana, we used DNA barcoding to identify market specimens. We sequenced the mitochondrial co1 gene for 132 samples collected from six markets, and compared our sequences to those available in the Barcode of Life Database (BOLD) and GenBank. Nearly 30% of the total sample diversity was represented by two species of Hammerhead Sharks (Sphyrna mokarran and S. lewini), both listed as Endangered by the International Union for Conservation of Nature (IUCN). Other significant portions of the samples included Sharpnose Sharks (23% - Rhizoprionodon spp.), considered Vulnerable in Brazilian waters due to unregulated gillnet fisheries, and the Smalltail Shark (17% - Carcharhinus porosus). We found that barcoding provides efficient and accurate identification of market specimens in Guyana, making this study the first in over thirty years to address Guyana’s coastal shark biodiversity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".