DNA Barcodes for Nearctic Auchenorrhyncha (Insecta: Hemiptera)
Bibliographic record
Abstract
BACKGROUND: Many studies have shown the suitability of sequence variation in the 5' region of the mitochondrial cytochrome c oxidase I (COI) gene as a DNA barcode for the identification of species in a wide range of animal groups. We examined 471 species in 147 genera of Hemiptera: Auchenorrhyncha drawn from specimens in the Canadian National Collection of Insects to assess the effectiveness of DNA barcoding in this group. METHODOLOGY/PRINCIPAL FINDINGS: Analysis of the COI gene revealed less than 2% intra-specific divergence in 93% of the taxa examined, while minimum interspecific distances exceeded 2% in 70% of congeneric species pairs. Although most species are characterized by a distinct sequence cluster, sequences for members of many groups of closely related species either shared sequences or showed close similarity, with 25% of species separated from their nearest neighbor by less than 1%. CONCLUSIONS/SIGNIFICANCE: This study, although preliminary, provides DNA barcodes for about 8% of the species of this hemipteran suborder found in North America north of Mexico. Barcodes can enable the identification of many species of Auchenorrhyncha, but members of some species groups cannot be discriminated. Future use of DNA barcodes in regulatory, pest management, and environmental applications will be possible as the barcode library for Auchenorrhyncha expands to include more species and broader geographic coverage.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
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".