Differentiation between <i>Aphis pomi</i> and <i>Aphis spiraecola</i> using multiplex real‐time PCR based on DNA barcode sequences
Bibliographic record
Abstract
Abstract The green apple aphid ( Aphis pomi) and the spirea aphid ( Aphis spiraecola ) are pests of apples in North America. Although management regimes exist to effectively control these pests, they differ significantly because of varying susceptibility of each species to common pesticides and differences in their life cycles. Therefore, accurate identification of the species present is essential for pest control. However, the identification process is complicated because of the morphological similarity between these two species. As a result, confusion between A. pomi and A. spiraecola often occurs. DNA barcoding has been proven to accurately identify species of Aphididae. A further study demonstrated that DNA barcodes could be used to accurately differentiate A. pomi and A. spiraecola . DNA barcoding represents an important step towards rapid identification of these pests as distinctions can be easily made between morphologically similar species as well as from eggs and immature individuals in addition to adults. However, samples must still be sent to specially equipped facilities for sequence analysis, which can take between several hours and days. Real‐time PCR is emerging as a useful tool for more rapid pest identification. The purpose of this study was to develop a real‐time PCR assay for differentiation of A.pomi from A. spiraecola based on DNA barcode sequences from the Barcode of Life Data System. This assay was designed on the portable SmartCycler II platform and can be used in field settings to differentiate these species quickly and accurately. It has the potential to be a valuable tool to improve pest management of A. pomi and A. spiraecola.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".