DNA Barcoding Southwestern Atlantic Skates: A 20-Year Effort in Building a Species Identification Library
Bibliographic record
Abstract
The skate fauna in the Southwest Atlantic Ocean (SWA; 34–55° S) is represented by ~32 species, many of which share external features that have led to misidentifications and deficient fishery statistics. The use of DNA barcoding to discriminate SWA skate species was explored after 20 years of surveys. COI sequences were subjected to distance-based neighbor-joining (NJ), maximum likelihood (ML), barcode index number (BIN), automatic barcode gap discovery (ABGD), and nucleotide diagnostic character (NDC) analyses. For widely distributed species, a haplotype network was built. Overall, 187 specimens and 31 egg cases from 26 skate species were barcoded. NJ and ML analyses showed that nearly all species exhibited unique barcodes or clusters of closely related haplotypes, except for Psammobatis normani/P. rudis and Dipturus trachyderma/D. argentinensis. The first pair was discriminated by NCD. BIN analysis recovered 17 groups, whereas ABGD recovered 23, better reflecting taxonomic diversity. In summary, 24 species were resolved by COI. Phylogeographic signals were observed for Amblyraja doellojuradoi and Zearaja brevicaudata. Compiling our results with data from BOLD, almost all the species occurring in the area possess barcodes, contributing to completing and curating the BOLD reference library, which constitutes an important tool for resolving taxonomic issues, tracing fishery products, and performing eDNA biomonitoring.
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 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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".