A 12S rDNA barcode reference library for Neotropical electric fishes (Teleostei, Gymnotiformes)
Bibliographic record
Abstract
DNA barcodes are instrumental for identifying species from tissues and environmental samples. Metabarcoding-based assessments of biodiversity depend on taxonomically accurate barcode sequence libraries that can be queried to determine which species are present in a sample. The mitochondrial 12S ribosomal RNA gene has been shown to function as a robust barcode for differentiating taxa and identifying species using environmental DNA (eDNA), but complete 12S reference barcodes remain scarce in public databases. We assembled a library of 12S rDNA reference barcodes for 152 species of Neotropical electric fishes from the order Gymnotiformes (Teleostei: Ostariophysi). These fishes are an abundant and diverse component of Neotropical freshwater ecosystems, but are often difficult to collect through conventional sampling approaches because of their nocturnal activity and association with dense substrates or deep-water habitats. This makes eDNA metabarcoding an especially suitable approach for their detection. We compiled a dataset comprising all gymnotiform families, with most sequences covering almost the entire 12S gene. Across the gene and within two widely used 12S eDNA metabarcoding loci (MiFish and teleo), our sequences show sufficient interspecific divergence to discriminate the majority of knifefish species included in our analyses. This dataset therefore provides a valuable new set of resources for differentiating among gymnotiform species and identifying them in environmental DNA samples.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".