Effect of nanopesticides (azoxystrobin and bifenthrin) on the phenolic content and metabolic profiles of strawberries (<i>Fragaria</i> × <i>ananassa</i>)
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
Abstract BACKGROUND Nanoencapsulation has opened promising fields of innovation for pesticides. Conventional pesticides can cause side effects on plant metabolism. To date, the effect of nanoencapsulated pesticides on plant phenolic contents has not been reported. RESULTS In this study, a comparative evaluation of the phenolic contents and metabolic profiles of strawberries was performed for plants grown under controlled field conditions and treated with two separate active ingredients, azoxystrobin and bifenthrin, loaded into two different types of nanocarriers (Allosperse® polymeric nanoparticles and SiO 2 nanoparticles). There were small but significant decreases of the total phenolic content (9%) and pelargonidin 3‐glucoside content (6%) in strawberries treated with the nanopesticides. An increase of 31% to 125% was observed in the levels of gallic acid, quercetin, and kaempferol in the strawberries treated with the nanoencapsulated pesticides compared with the conventional treatments. The effects of the nanocarriers on the metabolite and phenolic profiles was identified by principal component analysis. CONCLUSION Overall, even though the effects of nanopesticides on the phenological parameters of strawberry plants were not obvious, there were significant changes to the plants at a molecular level. In particular, nanocarriers had some subtle effects on plant health and fruit quality through variations in total and individual phenolics in the fruits. Further research will be needed to assess the impact of diverse nanopesticides on other groups of plant metabolites. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.
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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 | 0.001 |
| 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) | 0.000 | 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