Using DNA barcodes to connect adults and early life stages of marine fishes from the Yucatan Peninsula, Mexico: potential in fisheries management
Bibliographic record
Abstract
Barcoding has proven a useful tool in the rapid identification of all life stages of fish species. Such information is of critical importance for fisheries management and conservation, especially in high-diversity regions, such as Mexico’s marine waters, where more than 2200 species occur. The present study reports the barcode analysis of 1392 specimens from the Yucatan Peninsula, corresponding to 610 adults and juveniles, 757 larvae and 25 eggs, representing 181 species (179 teleosts and 2 rays), 136 genera and 74 families. Barcoding results revealed major range extensions and overlooked taxa, including three sympatric species of Albula (one likely undescribed) and a new taxon of Floridichthys. In total, six species of eggs and 34 species of larvae were identified through their barcode match with adults. These cases enabled the first discrimination of the larvae of four species of Eucinostomus, and new information about spawning locality and time was obtained from egg records for the hogfish, Lachnolaimus maximus, which is one of the most commercially important species in the Mexican Caribbean. Also, barcodes revealed mistakes in species recognition during a sport-fish contest. In the future, barcodes will help avoid similar errors and protect rare or endangered species, and will aid regulation of fisheries quotas.
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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".