Complete DNA barcode reference library for a country's butterfly fauna reveals high performance for temperate Europe
Bibliographic record
Abstract
DNA barcoding aims to accelerate species identification and discovery, but performance tests have shown marked differences in identification success. As a consequence, there remains a great need for comprehensive studies which objectively test the method in groups with a solid taxonomic framework. This study focuses on the 180 species of butterflies in Romania, accounting for about one third of the European butterfly fauna. This country includes five eco-regions, the highest of any in the European Union, and is a good representative for temperate areas. Morphology and DNA barcodes of more than 1300 specimens were carefully studied and compared. Our results indicate that 90 per cent of the species form barcode clusters allowing their reliable identification. The remaining cases involve nine closely related species pairs, some whose taxonomic status is controversial or that hybridize regularly. Interestingly, DNA barcoding was found to be the most effective identification tool, outperforming external morphology, and being slightly better than male genitalia. Romania is now the first country to have a comprehensive DNA barcode reference database for butterflies. Similar barcoding efforts based on comprehensive sampling of specific geographical regions can act as functional modules that will foster the early application of DNA barcoding while a global system is under development.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".