First insights towards RNAi-based management of the pollen beetle Brassicogethes viridescens, with risk assessment against model non-target pollinator and biocontrol insects
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 |
| Science and technology studies | 0.001 | 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 it