Influence of Sediment Grain Size on Elutriate Toxicity of Inorganic Nanomaterials
Bibliographic record
Abstract
Abstract Knowledge concerning the ecotoxic effects of nanomaterials, chemical structures with novel properties owing to their small sizes (1 to 100 nm), is wanting and deserves to be documented more fully. In this study we conducted testing with the MARA (microbial array for risk assessment) assay-an 11 microbial species 96-well microplate toxicity test measuring growth inhibition-to determine the toxic potential of four metallic nanopowders (MNPs): copper zinc iron oxide, samarium (III) oxide, erbium (III) oxide, and holmium (III) oxide. MTC (microbial toxicity concentration) endpoint values showed a range of toxicity responses generated by individual strains that was MNP-specific. Cluster analysis undertaken with the (n = 11) MTC values of the four MNPs, reflecting a toxic fingerprint proper to each nanochemical, indicated that their modes of action may be different. Experiments were also conducted with an artificial sediment, composed of varying concentrations of silica sand and kaolin (fine particles < 0.004 mm), spiked with each MNP to assess the contribution of fine particles on the resulting elutriate toxicity. The latter was shown to increase as fines contents decreased, except for CuZnFeO where no particular trends were observed. Toxicity testing was then undertaken with each MNP spiked into natural Saint Lawrence River freshwater sediments displaying low, medium, and high fines contents. Once again, analogous results to those obtained with the artificial sediment experiments were observed for MNP elutriate toxicity. Overall, MARA bioassay data indicate that MNP toxicity can be modulated by sediment grain size and that resulting adverse effects on aquatic biota will in part depend on such sediment characteristics.
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How this classification was reachedexpand
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 | 0.012 | 0.001 |
| 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 | 0.001 | 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 teacher head, 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".