Genetic Patterns in European Geometrid Moths Revealed by the Barcode Index Number (BIN) System
Bibliographic record
Abstract
BACKGROUND: The geometrid moths of Europe are one of the best investigated insect groups in traditional taxonomy making them an ideal model group to test the accuracy of the Barcode Index Number (BIN) system of BOLD (Barcode of Life Datasystems), a method that supports automated, rapid species delineation and identification. METHODOLOGY/PRINCIPAL FINDINGS: This study provides a DNA barcode library for 219 of the 249 European geometrid moth species (88%) in five selected subfamilies. The data set includes COI sequences for 2130 specimens. Most species (93%) were found to possess diagnostic barcode sequences at the European level while only three species pairs (3%) were genetically indistinguishable in areas of sympatry. As a consequence, 97% of the European species we examined were unequivocally discriminated by barcodes within their natural areas of distribution. We found a 1:1 correspondence between BINs and traditionally recognized species for 67% of these species. Another 17% of the species (15 pairs, three triads) shared BINs, while specimens from the remaining species (18%) were divided among two or more BINs. Five of these species are mixtures, both sharing and splitting BINs. For 82% of the species with two or more BINs, the genetic splits involved allopatric populations, many of which have previously been hypothesized to represent distinct species or subspecies. CONCLUSIONS/SIGNIFICANCE: This study confirms the effectiveness of DNA barcoding as a tool for species identification and illustrates the potential of the BIN system to characterize formal genetic units independently of an existing classification. This suggests the system can be used to efficiently assess the biodiversity of large, poorly known assemblages of organisms. For the moths examined in this study, cases of discordance between traditionally recognized species and BINs arose from several causes including overlooked species, synonymy, and cases where DNA barcodes revealed regional variation of uncertain taxonomic significance.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".