Molecular Taxonomy of South Africa’s Catsharks: How Far Have We Come?
Bibliographic record
Abstract
The ability to correctly identify specimens at the species level is crucial for assessing and conserving biodiversity. Despite this, species-specific data are lacking for many of South Africa’s catsharks due to a high level of morphological stasis. As comprehensive and curated DNA reference libraries are required for the reliable identification of specimens from morphologically similar species, this study reviewed and contributed to the availability of cytochrome c oxidase subunit I (COI) and nicotinamide adenine dehydrogenase subunit 2 (NADH2) sequences for South Africa’s catsharks. A molecular taxonomic approach, implementing species delimitation and specimen assignment methods, was used to assess and highlight any taxonomic uncertainties and/or errors in public databases. The investigated species were summarised into 47 molecular operational taxonomic units (MOTUs), with some conflicting specimen assignments. Two Apristurus specimens sampled in this study remained unidentified, revealing the presence of previously undocumented genetic diversity. In contrast, haplotype sharing within Haploblepharus—attributed to nucleotide ambiguities—resulted in the delimitation of three congeners into a single MOTU. This study reveals that molecular taxonomy has the potential to flag undocumented species and/or misidentified specimens, and further highlights the need to implement integrated taxonomic assessments on catsharks that represent an irreplaceable component of biodiversity in the region.
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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 | 0.006 | 0.008 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".