DNA Barcoding Indonesian freshwater fishes: challenges and prospects
Bibliographic record
Abstract
Abstract With 1172 native species, the Indonesian ichthyofauna is among the world’s most speciose. Despite that the inventory of the Indonesian ichthyofauna started during the eighteen century, the numerous species descriptions during the last decades highlight that the taxonomic knowledge is still fragmentary. Meanwhile, the fast increase of anthropogenic perturbations during the last decades is posing serious threats to Indonesian biodiversity. Indonesia, however, is one of the major sources of export for the international ornamental trade and home of several species of high value in aquaculture. The development of new tools for species identification is urgently needed to improve the sustainability of the exploitation of the Indonesian ichthyofauna. With the aim to build comprehensive DNA barcode libraries, the co-authors have started a collective effort to DNA barcode all Indonesian freshwater fishes. The aims of this review are: (1) to produce an overview of the ichthyological researches conducted so far in Indonesia, (2) to present an updated checklist of the freshwater fishes reported to date from Indonesia’s inland waters, (3) to highlight the challenges associated with its conservation and management, (4) to present the benefits of developing comprehensive DNA barcode reference libraries for the conservation of the Indonesian ichthyofauna.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".