Barcoding Bugs: DNA-Based Identification of the True Bugs (Insecta: Hemiptera: Heteroptera)
Bibliographic record
Abstract
BACKGROUND: DNA barcoding, the analysis of sequence variation in the 5' region of the mitochondrial cytochrome c oxidase I (COI) gene, has been shown to provide an efficient method for the identification of species in a wide range of animal taxa. In order to assess the effectiveness of barcodes in the discrimination of Heteroptera, we examined 344 species belonging to 178 genera, drawn from specimens in the Canadian National Collection of Insects. METHODOLOGY/PRINCIPAL FINDINGS: Analysis of the COI gene revealed less than 2% intra-specific divergence in 90% of the taxa examined, while minimum interspecific distances exceeded 3% in 77% of congeneric species pairs. Instances where barcodes fail to distinguish species represented clusters of morphologically similar species, except one case of barcode identity between species in different genera. Several instances of deep intraspecific divergence were detected suggesting possible cryptic species. CONCLUSIONS/SIGNIFICANCE: Although this analysis encompasses 0.8% of the described global fauna, our results indicate that DNA barcodes will aid the identification of Heteroptera. This advance will be useful in pest management, regulatory and environmental applications and will also reveal species that require further taxonomic research.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".