A new set of primers for <scp>COI</scp> amplification from freshwater microcrustaceans
Bibliographic record
Abstract
Despite the contribution of DNA barcoding towards understanding the biodiversity and distribution of species, the success of COI amplification has been quite variable when it comes to freshwater zooplankton (Elías-Gutiérrez & Valdez-Moreno 2008; Jeffery et al. 2011). Some genera of microcrustaceans seem to be more difficult to amplify than others. For example, Macrothrix, Scapholeberis, Diaphanosoma and cyclopoids have yielded limited results. Among several possible reasons for the inability to barcode freshwater microcrustaceans is that there does not exist a specific set of primers for COI amplification. To this end, we developed a zooplankton - specific set of primers, which significantly increased average amplification success (20% increase). With these primers, we observed an overall success of over 70% for Sididae and Chydoridae, and more than 80% for Daphniidae, Moinidae, Bosminidae, Macrothricidae, Ilyocryptidae and Diaptomidae. We also demonstrate a simple alteration to a common specimen fixation method that increases the overall recovery of barcodes from freshwater zooplankton. Collectively, we believe our results will greatly aid the recovery of barcodes from these difficult groups.
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How this classification was reachedexpand
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) | 0.001 | 0.001 |
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; both teacher heads agree on what is shown here.
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".