Phosphorus Availability Alters the Effects of Silver Nanoparticles on Periphyton Growth and Stoichiometry
Bibliographic record
Abstract
Exposure to silver nanoparticles (AgNPs) may alter the structure and function of freshwater ecosystems. However, there remains a paucity of studies investigating the effects of AgNP exposure on freshwater communities in the natural environment where interactions with the ambient environment may modify AgNP toxicity. We used nutrient diffusing substrates to determine the interactive effects of AgNP exposure and phosphorus (P) enrichment on natural assemblages of periphyton in three Canadian Shield lakes. The lakes were all phosphorus poor and spanned a gradient of dissolved organic carbon availability. Ag slowly accumulated in the exposed periphyton, which decreased periphyton carbon and chlorophyll a content and increased periphyton C:P and N:P in the carbon rich lakes. We found significant interactions between AgNP and P treatments on periphyton carbon, autotroph standing crop and periphyton stoichiometry in the carbon poor lake such that P enhanced the negative effects of AgNPs on chlorophyll a and lessened the impact of AgNP exposure on periphyton stoichiometry. Our results contrast with those of other studies demonstrating that P addition decreases metal toxicity for phytoplankton, suggesting that benthic and pelagic primary producers may react differently to AgNP exposure and highlighting the importance of in situ assays when assessing potential effects of AgNPs in fresh waters.
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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".