A needle in a haystack: a multigene TaqMan assay for the detection of Asian gypsy moths in bulk pheromone trap samples
Bibliographic record
Abstract
The Asian gypsy moth (AGM) is considered a very serious invasive threat in North America. For this reason, it is subjected to a bio-surveillance program that includes an extensive network of pheromone traps. For regulatory purposes, the term “AGM” designates a group of Asian Lymantria species and subspecies, comprising two L. dispar subspecies ( L. d. asiatica and L. d. japonica ), and three closely related species ( L. umbrosa, L. albescens and L. postalba ). These moths are attracted to the same pheromone as the European gypsy moth (EGM), L. dispar dispar , which is already established in North America and typically makes up the bulk of moths caught in gypsy moth pheromone traps. These different Lymantria taxa are difficult to distinguish from one another using morphological characters alone. Here, we designed a TaqMan triplex assay capable of detecting AGM in bulk pheromone trap samples. The assay targets SNPs found in three different mitochondrial genes. Using a DNA dilution series, we show that the assay can detect AGM taxa at AGM:EGM dilution ratios ≥ 1:1000. The assay was validated using batch DNA extractions of moth legs tested at a 1:100 AGM:EGM leg ratio, a proportion that is around the operational limit for a single pheromone trap. The assay provided correct identification for all AGM taxa tested. An experiment examining the integrity of DNA extracted from gypsy moths left in pheromone traps under field conditions for up to 4 months indicated that DNA quality remains sufficient, during that period, for the present assay to remain accurate.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".