Insects, Insecticides and Hormesis: Evidence and Considerations for Study
Bibliographic record
Abstract
Insects are ubiquitous, crucial components of almost all terrestrial and fresh water ecosystems. In agricultural settings they are subjected to, intentionally or unintentionally, an array of synthetic pesticides and other chemical stressors. These ecological underpinnings, the amenability of insects to laboratory and field experiments, and our strong knowledgebase in insecticide toxicology, make the insect-insecticide model an excellent one to study many questions surrounding hormesis. Moreover, there is practical importance for agriculture with evidence of pest population growth being accelerated by insecticide hormesis. Nevertheless, insects have been underutilized in studies of hormesis. Where hormesis hypotheses have been tested, results clearly demonstrate stimulatory effects on multiple taxa as measured through several biological endpoints, both at individual and population levels. However, many basic questions are outstanding given the myriad of chemicals, responses, and ecological interactions that are likely to occur.
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 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 | 0.013 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".