DNA barcoding reveals hidden diversity in the Neotropical freshwater fish<i>Piabina argentea</i>(Characiformes: Characidae) from the Upper Paraná Basin of Brazil
Bibliographic record
Abstract
BACKGROUND AND AIMS: We analyzed a small and wide geographically distributed Neotropical freshwater fish, the Piabina argentea from the Upper Paraná Basin, to check the hypothesis that this species is composed of more than one biological unit, since it has a limited dispersion, through the DNA barcode technique. MATERIALS AND METHODS: Partial mitochondrial COI and CytB gene sequences were obtained for 58 specimens drawn from 13 localities. RESULTS: Phylogenetic analysis revealed six major clusters of P. argentea. Kimura-two-parameter (K2P) genetic divergences among these six P. argentea clusters ranged from 2 to 5.6% and from 2.3 to 5.4% for COI and CytB genes, respectively, and these values were on average approximately nine times greater than intra-cluster K2P divergences. The fixation index (F(ST)) among clusters showed very high values and the haplotype network analysis displayed seven unconnected units. CONCLUSION: These results reinforce the hypothesis that the widely distributed P. argentea species concept as currently conceived actually represents more than one species (possibly six). These results demonstrate the efficacy of DNA barcoding for the discovery of hidden diversity in Neotropical freshwater fishes, and we conclude that barcoding is a useful tool for alpha taxonomy.
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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 | 0.001 |
| 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".