Probing diversity in freshwater fishes from Mexico and Guatemala with DNA barcodes
Bibliographic record
Abstract
The freshwater fish fauna of Mexico and Guatemala is exceptionally diverse with >600 species, many endemic. In this study, patterns of sequence divergence were analysed in representatives of this fauna using cytochrome c oxidase subunit 1 (COI) DNA barcodes for 61 species in 36 genera. The average divergence among conspecific individuals was 0.45%, while congeneric taxa showed 5.1% divergence. Three species of Poblana, each occupying a different crater lake in the arid regions of Central Mexico, have had a controversial taxonomic history but are usually regarded as endemics to a single lake. They possess identical COI barcodes, suggesting a very recent history of isolation. Representatives of the Cichlidae, a complex and poorly understood family, were well discriminated by barcodes. Many species of Characidae seem to be young, with low divergence values (<2%), but nevertheless, clear barcode clusters were apparent in the Bramocharax-Astyanax complex. The symbranchid, Opisthernon aenigmaticum, has been regarded as a single species ranging from Guatemala to Mexico, but it includes two deeply divergent barcode lineages, one a possible new endemic species. Aside from these special cases, the results confirm that DNA barcodes will be highly effective in discriminating freshwater fishes from Central America and that a comprehensive analysis will provide new important insights for understanding diversity of this fauna.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".