First insights towards RNAi-based management of the pollen beetle Brassicogethes viridescens, with risk assessment against model non-target pollinator and biocontrol insects
Bibliographic record
Abstract
The pollen beetle Brassicogethes viridescens has become an invasive pest to rapeseed crops in North America, especially Canada, the world’s most prolific grower of rapeseed. The use of conventional insecticides to control Brassicogethes spp. can lead to substantial insecticide resistance development in target pest populations and detrimental effects on non-target organisms in and around rapeseed crops. Therefore, economically and ecologically sustainable alternatives to conventional insecticides must be explored. Given the continued increases in production efficacy- and the nucleotide sequence-specific mode of action of dsRNA pesticide products, RNA pesticides represent a potential tool for use within the management of B. viridescens . We examined the insecticidal efficacy of dsRNA against B. viridescens , using transcripts of its intragenus relative Brassicogethes aeneus as a template for dsRNA design. In B. viridescens , we observed similar sensitivities to dsRNA compared to B. aeneus . Furthermore, survival assays using three model non-target species suggest highly selective insecticidal activity of the dsRNAs. Finally, we generated the first transcriptome draft for B. viridescens , which provides valuable information for future management needs against this pest species. Given these first insights towards sustainable RNAi-based management of B. viridescens , further work (different exposure methods, semi-field larval studies) is needed to develop RNAi-based approaches to managing B. viridescens in both European and North American rapeseed systems.
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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 | 0.000 |
| 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 | 0.001 |
| 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".