Conducting a battery of bioassays for gold nanoparticles to derive guideline value for the protection of aquatic ecosystems
Bibliographic record
Abstract
Gold nanoparticles (Au-NPs) are used in many applications, including the manufacture of products like cosmetics, paints, and electrochemical immunosensors, and in the detection, diagnosis, and treatment of tumors. However, there are no legal or recommended guidelines for protecting aquatic ecosystems from Au-NPs. In this study, we conducted a battery of bioassays and present toxicity values for two bacteria, one alga, one euglena, three cladoceran, and two fish species that were exposed to Au-NPs. Guideline values for protecting aquatic ecosystems from Au-NPs were derived using methods that are generally used to derive water-quality guidelines and are used in Australia, New Zealand, Canada, the European Community (EC), and the USA. Au-NPs had adverse effects on all test species, including growth inhibition of both bacteria, the alga, and the euglena; mortality and immobilization in the three cladocerans; and developmental malformations in the embryos and larvae of the two fish. Guideline values of 0.15 and 0.04 × 1010 particles/mL were derived for Au-NPs using a species sensitivity distribution (SSD) and assessment factor. The guideline value derived for Au-NPs using an assessment factor was more stringent than that derived using SSD. This is the first study to derive guideline values for nanoparticles in water environments.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".