DNA barcode identification of commercial fish sold in Mexican markets
Bibliographic record
Abstract
The substitution of high-value fish species for those of lower value is common practice. Although numerous studies have addressed this issue, few have been conducted in Mexico. In this study, we sought to identify fresh fillets of fish, sharks, and rays using DNA barcodes. We analyzed material from "La Viga" in Mexico City, and other markets located on the Gulf and Caribbean coasts of Mexico. From 134 samples, we obtained sequences from 129, identified to 9 orders, 28 families, 38 genera, and 44 species. The most common species were Seriola dumerili, Pangasianodon hypophthalmus, Carcharhinus falciformis, Carcharhinus brevipinna, and Hypanus americanus. Pangasianodon hypophthalmus was most commonly used as a substitute for higher-value species. The substitution rate was 18% of the total. A review of the conservation status of the specimens identified against the IUNC list enabled us to establish that some species marketed in Mexico are threatened: Makaira nigricans, Lachnolaimus maximus, Hyporthodus flavolimbatus, and Isurus oxyrinchus are classified as vulnerable; Lopholatilus chamaeleonticeps and Sphyrna lewini are endangered; and the status of Hyporthodus nigritus is critical. These results will demonstrate to the Mexican authorities that DNA barcoding is a reliable tool for species identification, even when morphological identification is difficult or impossible.
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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 | 0.002 | 0.002 |
| 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".