Do diamide insecticides have any impact on beneficial arthropods? A review
Bibliographic record
Abstract
Diamide insecticides with novel modes of action have taken a significant place in modern agricultural practices around the globe due to their excellent toxicological profile and effective target efficiency against crop pests. However, concerns surrounding their impact on nontarget organisms, particularly beneficial insects, have spurred widespread scrutiny. In this review, we extensively examine the off-target effects of diamide insecticides on demographic and life table parameters of insect pollinators, predators, parasitoids, and other beneficial organisms by understanding the mode of action as well as the critical comparison of the route of exposure of diamide insecticides between insect pests and beneficial arthropods. Furthermore, the differential susceptibility of different life stages of insects towards these insecticides, physiological impairment as well as detoxification mechanisms employed by natural enemies and pollinators to mitigate the adverse effects of diamide insecticides are also elucidated in detail. By critically evaluating the existing literature, it is clear that, while harnessing the benefits of diamides in insect pest management, the negative impacts on beneficial arthropods are not to be undermined. Future success in insect pest management will benefit from the identification of specific genes responsible for the detoxification of diamide pesticides in beneficial arthropods so as to harness the benefits, reduce their impact, and sustain their presence in the global agrochemical market. Furthermore, policy regulations and outreach activities are needed to inform end users on the judicious use of diamides in specific crop ecosystems.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".