Wide-ranging barcoding aids discovery of one-third increase of species richness in presumably well-investigated moths
Bibliographic record
Abstract
Rapid development of broad regional and international DNA barcode libraries have brought new insights into the species diversity of many areas and groups. Many new species, even within well-investigated species groups, have been discovered based initially on differences in DNA barcodes. We barcoded 437 collection specimens belonging to 40 pre-identified Palearctic species of the Elachista bifasciella group of moths (Lepidoptera, Elachistidae). Although the study group has been a subject of several careful morphological taxonomic examinations, an unexpectedly high number of previously undetected putative species is revealed, resulting in a 34% rise in species number in the study area. The validity of putative new species was subsequently supported with diagnostic morphological traits. We show that DNA barcodes provide a powerful method of detecting potential new species even in taxonomic groups and geographic areas that have previously been under considerable morphological taxonomic scrutiny.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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; 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".