Impacts of UV light on the effects of either conventional or nano-enabled azoxystrobin on Daphnia magna
Bibliographic record
Abstract
Agri-chemicals such as fungicides are applied in natural settings and hence are exposed to the environment's ultraviolet (UV) light. Recently, many fungicides in commerce are being modified as nano-enabled formulations to increase agricultural productivity and reduce potential off-target effects. The present study investigated the impacts of sunlight-grade UV emission on the effects of either conventional or nano-enabled azoxystrobin (Az or nAz, respectively), a commonly applied agricultural fungicide, on Daphnia magna. Daphnids were exposed to increasing concentrations of Az or nAz under either full-spectrum (Vis) or full-spectrum Vis + UV (Vis + UV) lighting regimes to evaluate LC50s. Az LC50 was calculated at 268.8 and 234.2 μg/L in Vis or Vis + UV, respectively, while LC50 for nAz was 485.6 and 431.0 μg/L under Vis or Vis + UV light, respectively. Daphnids were exposed to 10% LC50 of either Az or nAz under Vis or Vis + UV lighting regime for 48 h or 21 d (acute and chronic, respectively). By 48 h, both Az and nAz reduced O2 consumption and increased TBARS. Heart rate was increased in Az-exposed daphnids but not in nAz groups. Neither of the two chemicals impacted thoracic limb activity. In 21 d exposures, Az significantly reduced biomass production and fecundity, but nAz groups were not significantly different from controls. The results of the present study demonstrate that conventional Az is more toxic to D. magna at lethal and sub-lethal levels in acute and chronic exposures, and sunlight strength UV can potentiate both acute and chronic effects of Az and nAz on D. magna.
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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".