DNA Barcoding Indonesian freshwater fishes: challenges and prospects
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it