An update of the Worldwide Integrated Assessment (WIA) on systemic insecticides. Part 1: new molecules, metabolism, fate, and transport
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
With the exponential number of published data on neonicotinoids and fipronil during the last decade, an updated review of literature has been conducted in three parts. The present part focuses on gaps of knowledge that have been addressed after publication of the Worldwide Integrated Assessment (WIA) on systemic insecticides in 2015. More specifically, new data on the mode of action and metabolism of neonicotinoids and fipronil, and their toxicity to invertebrates and vertebrates, were obtained. We included the newly detected synergistic effects and/or interactions of these systemic insecticides with other insecticides, fungicides, herbicides, adjuvants, honeybee viruses, and parasites of honeybees. New studies have also investigated the contamination of all environmental compartments (air and dust, soil, water, sediments, and plants) as well as bees and apicultural products, food and beverages, and the exposure of invertebrates and vertebrates to such contaminants. Finally, we review new publications on remediation of neonicotinoids and fipronil, especially in water systems. Conclusions of the previous WIA in 2015 are reinforced; neonicotinoids and fipronil represent a major threat worldwide for biodiversity, ecosystems, and all the services the latter provide.
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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