Cracking the glass‐perchlet code: Integrative taxonomy uncovers high species‐level diversity within the glass‐perchlet genus <i>Ambassis</i> (Ambassidae) in tropical Asia
Bibliographic record
Abstract
Abstract Glass‐perchlets of the genus Ambassis (Teleostei; Ambassidae) form an important component of the brackish and marine coastal fish communities of tropical Asia. However, their species‐level diversity is still poorly documented because of the absence of recent taxonomic revisions in this region and the limited availability of specimens for research. In addition, long‐standing taxonomic and nomenclatural issues complicate the studies of this genus. Herein, we examine the diversity of Ambassis in Peninsular Malaysia using an integrative taxonomic approach and a large set of recently collected specimens from this region. Our initial morphological observations of 260 specimens revealed the presence of eight species, identified as Ambassis dussumieri, Ambassis interrupta, Ambassis kopsii, Ambassis macracanthus, Ambassis nalua, Ambassis octava, Ambassis urotaenia and Ambassis vachellii. We then sequenced the barcode fragment of the mitochondrial cytochrome c oxidase subunit I (COI) gene for 122 of our specimens, representing all eight morpho‐species. Automatic species delimitation methods recovered nine Molecular Operational Taxonomic Units (MOTUs) because A. interrupta is made of two MOTUs. Morphological re‐examination within A. interrupta detected variation at one character, congruent with molecular delimitation. Overall, our integrative approach unveiled rich species‐level diversity within the genus Ambassis in Peninsular Malaysia, with the presence of nine species. Further comparisons between our COI dataset and the COI sequences archived in the Barcode of Life Data System (BOLD) from specimens of Ambassis broadly collected in tropical Asian regions, indicated regional‐scale hidden diversity and identification conflicts, triggering the need for a complete taxonomic revision of this genus.
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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 | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| 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".