Bioinsecticide synergy: The good, the bad and the unknown
Bibliographic record
Abstract
Synergy between certain conventional chemical insecticides has been known for decades. However, heightened awareness and interest in bioinsecticides (microbials, botanicals and arthropod venoms) have led to numerous studies demonstrating synergy between bioinsecticides and conventional insecticides, between different bioinsecticides, and among specific constituents in botanicals, which are themselves chemically complex. At the same time, bioinsecticides have often been shown to be less deleterious to non-target organisms, particularly natural enemies and pollinators, although they are not entirely without negative impacts. However, the influence of synergy among these compounds, mixtures of bioinsecticides, or combinations of bioinsecticides and conventional insecticides on non-target species remains relatively unexplored. The taxonomic diversity of target (pest) insects for which such synergy has been documented suggests that this action could also occur in non-target species. However, the impact of this synergy on non-targets in actual field conditions remains difficult to predict. • Synergy by bioinsecticides is common against diverse pest arthropods. • It is also common across many modes of action, and appears to be both dose-specific and idiosyncratic. • Synergy can occur between bioinsecticides and conventional ones. • It can also occur between types of bioinsecticides. • Little work has characterized synergy of bioinsecticides in non-target arthropods.
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 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 | 0.001 | 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 | 0.001 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".