Real‐time <scp>PCR</scp> for identification of the soybean aphid, <i>Aphis glycines</i> Matsumura
Bibliographic record
Abstract
Abstract The soybean aphid (Aphis glycines Matsumura) is an economically significant pest in North America, causing extensive damage to soybean crops through direct feeding damage and disease transmission. If unchecked, this pest could cause billions of dollars of damage to soybean crops. Identification of the soybean aphid can be difficult due to its small size, complex life cycle and morphological plasticity. Generally, an expert is required to identify a specimen. Additionally, identification of some life stages, such as eggs, is impossible. DNA barcoding has been successfully used to differentiate aphid species, including A. glycines, based on sequencing of a standardized gene region. Although this method represents an important step towards accurate identification, samples must still be sent to specialized facilities for analysis. Using existing DNA barcode sequences in the publically accessible Barcode of Life Data System (BOLD; www.boldsystems.org ), species‐specific differences were identified and used to develop a real‐time PCR assay to identify soybean aphids. This assay can be run on portable systems for rapid, accurate and simple identification in the field. The use of a non‐destructive DNA extraction protocol allows the original insect to be vouchered and therefore available for further study if necessary. This work represents an important step in soybean aphid management.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".