Barcoding the Neotropical freshwater fish fauna using a new pair of universal COI primers with a discussion of primer dimers and M13 primer tails
Bibliographic record
Abstract
Designing primers for DNA barcoding is a significant challenge for the rich Neotropical fish fauna, which is comprised of ∼6000 species. Previously, researchers required multiple pairs of PCR primers or primer cocktails to obtain standard COI (i.e., mitochondrial cytochrome c oxidase subunit I) barcode sequences from assemblages of freshwater fish in this region. To simplify DNA barcoding and metabarcoding studies of Neotropical freshwater fish, we present a new pair of COI primers, which have yielded high quality barcodes across six teleost orders-Characiformes, Cichliformes, Cyprinodontiformes, Gymnotiformes, Siluriformes, and Synbranchiformes-native to South America. Following previous fish barcoding studies, we also tailed our primers with M13 forward and reverse primers to facilitate the DNA sequencing process. Although this practice generates primer dimers, we obtained complete and high quality COI barcode sequences for all samples. We discuss the problem of primer dimers and suggest strategies for neutralizing their influence on data quality.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".