<scp>DNA</scp>barcoding<scp>N</scp>eotropical fishes: recent advances from the<scp>P</scp>ampa<scp>P</scp>lain,<scp>A</scp>rgentina
Bibliographic record
Abstract
The fish fauna of the Pampa Plain, the southernmost distribution range of many Neotropical species, was barcoded in this study. COI sequences were analysed by means of distance (K2P/NJ) and character-based (ML) models, as well as the Barcode Index Number (BIN). K2P/NJ analysis was able to discriminate among all previously identified species while also revealing the likely occurrence of two cryptic species that were further supported by BIN and ML analyses. On the other hand, both BIN and ML were not able to discriminate between two species of Rineloricaria. Despite the small genetic divergence between A. cf. pampa and A. eigenmanniorum, a tight array of haplotypes was observed for each species in both the distance and character-based methods. Deep intraspecific divergences were detected in Cnesterodon decemmaculatus (5%) and Salminus brasiliensis (6%). For Salminus brasiliensis, these findings were further supported by character-based (ML) evidence and meristic and morphological data. Our results also showed that Pampa Plain representatives of Salminus brasiliensis, Rhamdia quelen, Hoplias malabaricus, Synbranchus marmoratus, Australoheros facetus, Oligosarcus jenynsii and Corydoras paleatus differed by more than 3% from their conspecifics from other parts of South America. Overall, this study was able to highlight the likely occurrence of a cryptic species in Salminus brasiliensis and also illustrate the strong geographical structure in the COI sequence composition of seven fish species from South America.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".