Validation of a molecular tool for detecting <i>Diadegma</i> spp. (Hymenoptera: Ichneumonidae) parasitism in diamondback moth, <i>Plutella xylostella</i> (Lepidoptera: Plutellidae) larvae in North America
Bibliographic record
Abstract
The diamondback moth, Plutella xylostella L. (Lepidoptera: Plutellidae), is a major pest affecting Brassica crops worldwide. In North America, P. xylostella outbreaks have caused substantial economic losses in canola (Brassica napus L. and Brassica rapa L.) and brassicaceous vegetable (Brassica oleracea L.) crops. Natural enemies, including the parasitoid Diadegma insulare (Hymenoptera: Ichneumonidae), play a key role in controlling P. xylostella populations. To better understand the distribution and impact of Diadegma spp., we adopted Diadegma-specific primers, developed originally for haplotyping, and used a molecular screening approach to detect parasitism of P. xylostella larvae collected across Canada and the United States. The primer set was first tested on D. insulare adults and laboratory parasitized P. xylostella to confirm amplification and specificity and determine sensitivity. Screening was then conducted on 421 field-collected larvae from 45 localities to detect and estimate parasitism rates and assess geographic variability. The results revealed notable variation across collection sites in parasitism of P. xylostella larvae by Diadegma spp., with more parasitized larvae collected from field sites in Canada than from field sites in the United States. Variation in parasitism across collection sites could be the result of differences in climate, agricultural practices, and insecticide use between agricultural systems. This research demonstrates the utility of molecular screening to detect parasitism of P. xylostella larvae and highlights potential differences in the impact of Diadegma spp. on the population dynamics of P. xylostella in Canada and the United States.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".