Cryptic diversity of the subfamily Calaphidinae (Hemiptera: Aphididae) revealed by comprehensive DNA barcoding
Bibliographic record
Abstract
Aphids are a species rich group comprising many important pests. However, species identification can be very difficult for aphids due to their morphological ambiguity. DNA barcoding has been widely adopted for rapid and reliable species identification as well as cryptic species detection. In this study, we investigated cryptic diversity in the subfamily Calaphidinae (Hemiptera: Aphididae) based on 899 sequences of cytochrome c oxidase I (COI) for 115 morphospecies (78 species collected in this study and sequences of 73 species downloaded from Genbank). Among these 115 morphospecies, DNA barcoding results of 90 (78.3%) species were identical to results of morphological identification. However, 25 (21.7%) morphospecies showed discrepancies between DNA barcoding and traditional taxonomy. Among these 25 discordances, a total of 15 cryptic species were identified from 12 morphospecies. We also found three morphologically distinct species pairs that sharing DNA barcoding. Based on molecular operational taxonomic unit (MOTU) estimation, we discussed on species delimitation threshold value for these taxa. Our findings confirm that Calaphidinae has high cryptic diversity even though aphids are relatively well-studied.
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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".