DNA barcoding discriminates freshwater fishes from southeastern Nigeria and provides river system-level phylogeographic resolution within some species
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND AND AIMS: Fishes are the main animal protein source for human beings and play a vital role in aquatic ecosystems and food webs. Fish identification can be challenging, especially in the tropics (due to high diversity), and this is particularly true for larval forms or fragmentary remains. DNA barcoding, which uses the 5' region of the mitochondrial cytochrome c oxidase subunit I (COI) as a target gene, is an efficient method for standardized species-level identification for biodiversity assessment and conservation, pending the establishment of reference sequence libraries. MATERIALS AND METHODS: In this study, fishes were collected from three rivers in southeastern Nigeria, identified morphologically, and imaged digitally. DNA was extracted, PCR-amplified, and the standard barcode region was bidirectionally sequenced for 363 individuals belonging to 70 species in 38 genera. All specimen provenance data and associated sequence information were recorded in the barcode of life data systems (BOLD; www.barcodinglife.org ). Analytical tools on BOLD were used to assess the performance of barcoding to identify species. RESULTS: Using neighbor-joining distance comparison, the average genetic distance was 60-fold higher between species than within species, as pairwise genetic distance estimates averaged 10.29% among congeners and only 0.17% among conspecifics. Despite low levels of divergence within species, we observed river system-specific haplotype partitioning within eight species (11.4% of all species). CONCLUSION: Our preliminary results suggest that DNA barcoding is very effective for species identification of Nigerian freshwater fishes.
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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