Copper toxicity to Folsomia candida in different soils: a comparison between nano and conventional formulations
Bibliographic record
Abstract
Environmental context Nanotechnology has great potential for managing agricultural plant disease. This study compares effects of various nano and conventional copper formulations on Collembola Folsomia candida, a model arthropod often used to test soil pollutants. Although toxicity varied between formulations, with a nanopesticide showing one of the highest toxicities, the differences were mainly due to the active copper compound rather than its nano formulation. Abstract Nanotechnology has recently become a major asset in agricultural practices owing to the improvement and provision of solutions for plant disease management, especially in the form of nanopesticides. These products are considered as one of the main direct sources of nanomaterials in terrestrial ecosystems, and for that reason, it is essential to understand and evaluate their behaviour and toxicity in the environment. In addition, the panoply of similar compounds makes it difficult to accurately evaluate if different formulations may induce different effects on non-target organisms. We aim to compare the toxicity and bioavailability of different copper formulations (nano and non-nano) applied to the soil using the model organism Folsomia candida. Reproduction tests were performed in two different soils (Lufa 2.1 and 2.2) with two equilibrium times after spiking (0 and 48 h). Depending on the copper form present in the formulation, the half maximal effective concentration (EC50) values of the non-nano pesticides varied from 274 mg Cu kg-1 in Champion® to 3030 mg Cu kg-1 in Cupravit® in Lufa 2.2 and 48 h equilibrium. The nano formulation displayed one of the highest toxicities to collembolans, with an EC50 of 156 mg Cu kg-1. Regarding the soil type, a higher toxicity in Lufa 2.1 was observed probably related to the soil characteristics, namely lower pH and lower organic content, which increase the metal solubility and consequently the bioavailability. The only exception was the nanopesticide Kocide 3000®, whose toxicity was higher in Lufa 2.2. This study emphasises the importance of understanding the links between metal bioavailability and toxicity to support the more robust environmental risk assessment of different Cu fungicide formulations.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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 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".