Large geographic distance versus small DNA barcode divergence: Insights from a comparison of European to South Siberian Lepidoptera
Bibliographic record
Abstract
Spanning nearly 13,000 km, the Palearctic region provides an opportunity to examine the level of geographic coverage required for a DNA barcode reference library to be effective in identifying species with broad ranges. This study examines barcode divergences between populations of 102 species of Lepidoptera from Europe and South Siberia, sites roughly 6,000 km apart. While three-quarters of these species showed divergence between their Asian and European populations, these divergence values ranged between 0-1%, distinctly less than the distance to the Nearest-Neighbor species in all but a few cases. Our results suggest that further taxonomic studies may be required for 16 species that showed either extremely low interspecific or high intraspecific variation. For example, seven species pairs showed low or no barcode divergence, but four of these cases are likely to reflect taxonomic over-splitting while the others involve species pairs that are either young or show evidence for introgression. Conversely, some of the nine species with deep intraspecific divergence at varied spatial levels may include overlooked species. Although these 16 cases require further investigation, our overall results indicate that barcode reference libraries based on records from one locality can be very effective in identifying specimens across an extensive geographic area.
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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) | 0.000 | 0.000 |
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 teacher head, 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".