DNA barcode library of megadiverse Austrian Noctuoidea (Lepidoptera) – a nearly perfect match of Linnean taxonomy
Bibliographic record
Abstract
The aim of the study was to establish a nationwide barcode library for the most diverse group of Austrian Lepidoptera, the Noctuoidea, with 5 families (Erebidae, Euteliidae, Noctuidae, Nolidae, Notodontidae) and around 690 species. Altogether, 3431 DNA barcode sequences from COI gene (cytochrome c oxidase 1) belonging to 671 species were gathered, with 3223 sequences >500 bp. The intraspecific divergence with a mean of only 0.17% is low in most species whereas interspecific distances to the Nearest Neighbour are significantly higher with an average of 4.95%. Diagnostic DNA barcodes were obtained for 658 species. Only 13 species (1.9% of the Austrian Noctuoidea) cannot be reliably identified from their DNA barcode (Setina aurita/Setina irrorella,Conisania leineri/Conisania poelli,Photedes captiuncula/Photedes minima,Euxoa obelisca/Euxoa vitta/Euxoa tritici,Mesapamaea secalella/Mesapamea secalis,Amphipoea fucosa/Amphipoea lucens). A similarly high identification performance was achieved by the Barcode Index (BIN) system. 671 species of Austrian Noctuoidea, representing 3202 records with BINs, are assigned to a total of 678 BINs. The vast majority of 649 species is placed into a single BIN, with only 13 species recognised as BIN-sharing (including the barcode sharing species above). Twenty-one species were assigned to more than one BIN and have to be checked for cryptic diversity in the future.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".