Chemical Aspects of Nanoparticle Ecotoxicology
Bibliographic record
Abstract
Nanoecotoxicology strives to understand the processes and mechanisms by which engineered nanoparticles (ENP) may exert toxic effects on aquatic organisms. Detailed knowledge of the chemical reactions of nanoparticles in the media and of their interactions with organisms is required to understand these effects. The processes of agglomeration of nanoparticles, of dissolution and release of toxic metal ions, and of production of reactive oxygen species (ROS) are considered in this article. Important questions concern the role of uptake of nanoparticles in various organisms, in contrast to uptake of ions released from nanoparticles and to nanoparticle attachment to organism surfaces. These interactions are illustrated for effects of silver nanoparticles (AgNP), cerium oxide (CeO2 NP) and titanium dioxide (TiO2 NP), on aquatic organisms, including algae, biofilms, fish cells and fish embryos.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".