Revisiting the ichthyodiversity of Java and Bali through <scp>DNA</scp> barcodes: taxonomic coverage, identification accuracy, cryptic diversity and identification of exotic species
Bibliographic record
Abstract
Among the 899 species of freshwater fishes reported from Sundaland biodiversity hotspot, nearly 50% are endemics. The functional integrity of aquatic ecosystems is currently jeopardized by human activities, and landscape conversion led to the decline of fish populations in several part of Sundaland, particularly in Java. The inventory of the Javanese ichthyofauna has been discontinuous, and the taxonomic knowledge is scattered in the literature. This study provides a DNA barcode reference library for the inland fishes of Java and Bali with the aim to streamline the inventory of fishes in this part of Sundaland. Owing to the lack of available checklist for estimating the taxonomic coverage of this study, a checklist was compiled based on online catalogues. A total of 95 sites were visited, and a library including 1046 DNA barcodes for 159 species was assembled. Nearest neighbour distance was 28-fold higher than maximum intraspecific distance on average, and a DNA barcoding gap was observed. The list of species with DNA barcodes displayed large discrepancies with the checklist compiled here as only 36% (i.e. 77 species) and 60% (i.e. 24 species) of the known species were sampled in Java and Bali, respectively. This result was contrasted by a high number of new occurrences and the ceiling of the accumulation curves for both species and genera. These results highlight the poor taxonomic knowledge of this ichthyofauna, and the apparent discrepancy between present and historical occurrence data is to be attributed to species extirpations, synonymy and misidentifications in previous studies.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".