Interactive Effects of Silver Nanoparticles and Phosphorus on Phytoplankton Growth in Natural Waters
Bibliographic record
Abstract
Increasing amounts of silver nanoparticles (AgNPs) are expected to enter the aquatic ecosystems where their effects on natural phytoplankton communities are poorly understood. We investigated the effects of AgNPs and its interactions with phosphorus (P) supply on the growth kinetics and stoichiometry of natural phytoplankton. Lake water was dosed with AgNPs (carboxy-functionalized capping agent; ∼10-nm particle size; ∼20% Ag w/w) at four different concentrations and five P concentrations and incubated in situ for 3 days. A treatment with ionic silver (AgNO3) was used as a positive control. We found that growth rates, calculated from changes in seston carbon and chlorophyll, responded significantly and interactively (p < 0.0001) to both AgNPs and P. AgNPs reduced the maximum phytoplankton growth rates by 11-85%. In the positive control, no or very little growth was observed. Inhibition of growth rates after exposure to Ag might be related to the reduction in chlorophyll and the inhibition of C and N acquisition rather than P uptake mechanisms. AgNPs, P supply and their interactions also significantly (p < 0.0001) reduced sestonic C:P and N:P ratios and increased C:N, C:Chl and cell-bound Ag stoichiometry. Our results indicate that fate and toxicity of AgNP will vary with phosphorus pollution level in aquatic ecosystems.
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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 | 0.000 | 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 | 0.001 |
| 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 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".