Influence of killing method on <scp>L</scp>epidoptera <scp>DNA</scp> barcode recovery
Bibliographic record
Abstract
The global DNA barcoding initiative has revolutionized the field of biodiversity research. Such large-scale sequencing projects require the collection of large numbers of specimens, which need to be killed and preserved in a way that is both DNA-friendly and which will keep voucher specimens in good condition for later study. Factors such as time since collection, correct storage (exposure to free water and heat) and DNA extraction protocol are known to play a role in the success of downstream molecular applications. Limited data are available on the most efficient, DNA-friendly protocol for killing. In this study, we evaluate the quality of DNA barcode (cytochrome oxidase I) sequences amplified from DNA extracted from specimens collected using three different killing methods (ethyl acetate, cyanide and freezing). Previous studies have suggested that chemicals, such as ethyl acetate and formaldehyde, degraded DNA and as such may not be appropriate for the collection of insects for DNA-based research. All Lepidoptera collected produced DNA barcodes of good quality, and our study found no clear difference in nucleotide signal strength, probability of incorrect base calling and phylogenetic utility among the three different treatment groups. Our findings suggest that ethyl acetate, cyanide and freezing can all be used to collect specimens for DNA analysis.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".