Probing planetary biodiversity with DNA barcodes: The Noctuoidea of North America
Bibliographic record
Abstract
This study reports the assembly of a DNA barcode reference library for species in the lepidopteran superfamily Noctuoidea from Canada and the USA. Based on the analysis of 69,378 specimens, the library provides coverage for 97.3% of the noctuoid fauna (3565 of 3664 species). In addition to verifying the strong performance of DNA barcodes in the discrimination of these species, the results indicate close congruence between the number of species analyzed (3565) and the number of sequence clusters (3816) recognized by the Barcode Index Number (BIN) system. Distributional patterns across 12 North American ecoregions are examined for the 3251 species that have GPS data while BIN analysis is used to quantify overlap between the noctuoid faunas of North America and other zoogeographic regions. This analysis reveals that 90% of North American noctuoids are endemic and that just 7.5% and 1.8% of BINs are shared with the Neotropics and with the Palearctic, respectively. One third (29) of the latter species are recent introductions and, as expected, they possess low intraspecific divergences.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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 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".