DNA BARCODING: CO1 DNA barcoding amphibians: take the chance, meet the challenge
Bibliographic record
Abstract
Although a mitochondrial DNA barcode has been shown to be of great utility for species identification and discovery in an increasing number of diverse taxa, caution has been urged with its application to one of the most taxonomically diverse vertebrate groups - the amphibians. Here, we test three of the perceived shortcomings of a CO1 DNA barcode's utility with a group of Holarctic amphibians: primer fit, sequence variability and overlapping intra- and interspecific variability. We found that although the CO1 DNA barcode priming regions were variable, we were able to reliably amplify a CO1 fragment from degenerate primers and primers with G-C residues at the 3' end. Any overlap between intra- and interspecific variation in our taxonomic sampling was due to introgressive hybridization (Bufo/Anaxyrus), complex genetics (Ambystoma) or incomplete taxonomy (Triturus). Rates of hybridization and species discovery are not expected to be greater for amphibians than for other vertebrate groups, and thus problems with the utility of using a single mitochondrial gene for species identification will not be specific to amphibians. Therefore, we conclude that there is greater potential for a CO1 barcode's use with amphibians than has been reported to date. A large-scale effort to barcode the amphibians of the world, using the same primary barcode region of CO1, will yield important findings for science and conservation.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.010 |
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".