Incorporating DNA barcodes into a multi-year inventory of the fishes of the hyperdiverse Lower Congo River, with a multi-gene performance assessment of the genus<i>Labeo</i>as a case study
Bibliographic record
Abstract
BACKGROUND AND AIMS: Here we describe preliminary efforts to integrate DNA barcoding into an ongoing inventory of the Lower Congo River (LCR) ichthyofauna. The 350 km stretch of the LCR from Pool Malebo to Boma includes the world's largest river rapids. The LCR ichthyofauna is hyperdiverse and rich in endemism due to high habitat heterogeneity, numerous dispersal barriers, and its downstream location in the basin. MATERIALS AND METHODS: We have documented 328 species from the LCR, 25% of which are thought to be endemic. In addition to detailing progress made to generate a reference sequence library of DNA barcodes for these fishes, we ask how DNA can be used at the current stage of the Fish Barcode of Life initiative, as a work in progress currently of limited utility to a wide audience. Two possibilities that we explore are the potential for DNA barcodes to generate discrete diagnostic characters for species, and to help resolve problematic taxa lacking clear morphologically diagnostic characters such as many species of the cyprinid genus Labeo, which we use as a case study. RESULTS: Our molecular analysis helped to clarify the validity of some species that were the subject of historical debate, and we were able to construct a molecular key for all monophyletic and morphologically recognizable species. Several species sampled from across the Congo Basin and widely distributed throughout Central and West Africa were recovered as paraphyletic based on our molecular data. CONCLUSION: Our study underscores the importance of generating reference barcodes for specimens collected from, or in close proximity to, type localities, particularly where species are poorly understood taxonomically and the extent of their geographical distributions have yet to be established.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 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".