The influence of surface waters on the bioavailability and toxicity of copper oxide nanoparticles to freshwater mussels
Bibliographic record
Abstract
The increased commercial use of copper oxide nanoparticles (nCuO) led to the release of nanoparticles in wastewaters potentially harming the aquatic biota. The purpose of this study was to determine the toxic action of nCuO and dissolved Cu (II) to Dreissena bugensis freshwater mussels placed in 4 types of surface waters: aquarium, green (high conductivity), brown (high organic carbon) and 10 % municipal effluent (high conductivity and anthropogenic source of organic carbon). Mussels were exposed to 50 µg/L of nCuO or Cu (II) for 96 h at 15 °C in the above waters. The results revealed that the total Cu loadings were higher in mussels placed in organic-rich waters (brown and effluent) and exposed to either forms of Cu. Tissue Cu contents were correlated with air-time survival, lipid peroxidation, protein-ubiquitin levels and DNA strand breaks. Both surface water types and Cu forms influenced Zn (II) mobilization, glutathione S-transferase activity and protein turnover (ubiquitin binding). Based on the surface water properties, Cu (II) was more influenced by the levels and origin of the organic carbon content while nCuO was more influenced by the total suspended solids. In conclusion the toxicity of nCuO could be influenced by surface waters properties expecially when similar physiological targets are impacts by these treatments.
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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) | 0.000 | 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 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".