Revisiting the Diversity of Barbonymus (Cypriniformes, Cyprinidae) in Sundaland Using DNA-Based Species Delimitation Methods
Bibliographic record
Abstract
Biodiversity hotspots often suffer from a lack of taxonomic knowledge, particularly those in tropical regions. However, accurate taxonomic knowledge is needed to support sustainable management of biodiversity, especially when it is harvested for human sustenance. Sundaland, the biodiversity hotspot encompassing the islands of Java, Sumatra, Borneo, and Peninsular Malaysia, is one of those. With more than 900 species, its freshwater ichthyofauna includes a large number of medium- to large-size species, which are targeted by inland fisheries. Stock assessment requires accurate taxonomy; however, several species groups targeted by inland fisheries are still poorly known. One of those cases is the cyprinid genus Barbonymus. For this study, we assembled a consolidated DNA barcode reference library for Barbonymus spp. of Sundaland, consisting of mined sequences from BOLD, as well as newly generated sequences for hitherto under-sampled islands such as Borneo. A total of 173 sequences were analyzed using several DNA-based species delimitation methods. We unambiguously detected a total of 6 Molecular Operational Taxonomic Units (MOTUs) and were able to resolve several conflicting assignments to the species level. Furthermore, we clarified the identity of MOTUs occurring in Java.
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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 | 0.001 |
| 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) | 0.000 | 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".