The efficiency of the <i>COI</i> gene as a DNA barcode and an overview of Orthoptera (Caelifera and Ensifera) sequences in the BOLD System
Bibliographic record
Abstract
Orthoptera, among the oldest and most numerous insect lineages, is an excellent model for evolutionary studies but has numerous taxonomic problems. To mitigate these issues, the cytochrome c oxidase subunit I ( COI), standardized with the DNA barcode for Metazoa, is increasingly used for specimen identification and species delimitation. We tested the performance of COI as a DNA barcode in Orthoptera, using two analyses based on intra- and inter-specific distances, barcode gap, and Probability of Correct Identification (PCI); and estimated species richness through Automatic Barcode Gap Discovery (ABGD) and Assemble Species by Automatic Partitioning (ASAP). We filtered all sequences of Orthoptera available in Barcode of Life Data System (BOLD) and used 11 605 COI sequences, covering 1 132 species, 226 genera, and 18 families. The overall average PCI was 73.86%. For 82.2% of genera, the barcode gap boxplots were classified as “good” or “intermediate”, indicating that COI can be effective as a DNA barcode in Orthoptera, although with varying efficiency depending on the need for more information. ABGD and ASAP inferred species richness similar to labels informed by BOLD for the suborders Caelifera and Ensifera. The representation of Orthoptera in the BOLD database and the results of these analyses are discussed.
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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 | 0.002 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".