DNA barcodes discriminate freshwater fishes from the Paraíba do Sul River Basin, São Paulo, Brazil
Bibliographic record
Abstract
BACKGROUND AND AIMS: Considering the promising use of DNA barcoding for species identification, the importance of the freshwater fish fauna of the Paraíba do Sul River Basin, and its advanced stage of degradation, the present study evaluated the effectiveness of DNA barcoding to identify the fish species in this basin. MATERIALS AND METHODS: A total of 295 specimens representing 58 species belonging to 40 genera, 17 families, and 5 orders were sequenced. RESULTS: The DNA barcodes discriminated all species analyzed without ambiguity. The results showed a pronounced difference between conspecific and congeneric pair-wise sequence comparisons, demonstrating the existence of a "barcode gap" for the species analyzed. The nearest-neighbor distance analysis showed only three cases with Kimura two-parameter values lower than a 2% divergence threshold. However, the patterns of divergence observed in each case remained sufficient to discriminate each species, revealing the accuracy of DNA barcoding even cases with relatively low genetic divergence. At the other extreme, three species displayed high genetic sequence divergence among conspecifics. For two cases, Characidium alipioi and Geophagus proximus, barcoding proved effective at flagging possible new species. For another case, Astyanax bimaculatus, the use of DNA barcoding of the comparison of shared freshwater fish fauna between different basins revealed itself as highly useful in disclosing that the previously identified A. bimaculatus "cluster A" probably represents the species Astyanax altiparanae. CONCLUSION: The present study is among the first to assess the efficiency of barcoding for the Brazilian freshwater fishes. The results demonstrate the utility of barcoding to identify the fauna from this basin, contribute to an enhanced understanding of the differentiation among species, and to help flag the presence of overlooked species.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".