A DNA barcode library for the butterflies of North America
Bibliographic record
Abstract
Although the butterflies of North America have received considerable taxonomic attention, overlooked species and instances of hybridization continue to be revealed. The present study assembles a DNA barcode reference library for this fauna to identify groups whose patterns of sequence variation suggest the need for further taxonomic study. Based on 14,626 records from 814 species, DNA barcodes were obtained for 96% of the fauna. The maximum intraspecific distance averaged 1/4 the minimum distance to the nearest neighbor, producing a barcode gap in 76% of the species. Most species (80%) were monophyletic, the others were para- or polyphyletic. Although 15% of currently recognized species shared barcodes, the incidence of such taxa was far higher in regions exposed to Pleistocene glaciations than in those that were ice-free. Nearly 10% of species displayed high intraspecific variation (>2.5%), suggesting the need for further investigation to assess potential cryptic diversity. Aside from aiding the identification of all life stages of North American butterflies, the reference library has provided new perspectives on the incidence of both cryptic and potentially over-split species, setting the stage for future studies that can further explore the evolutionary dynamics of this group.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".