Isotopic‐based evidence for reduced benthic contributions to fish after a whole‐lake addition of nanosilver
Bibliographic record
Abstract
Silver nanoparticles (AgNPs) are added as antibacterial and anti-odor agents to a wide range of textiles, with high potential for release into aquatic environments via domestic wastewater. Previous work demonstrating the negative impacts of AgNP exposure on periphyton production suggests benthic primary production could be reduced in aquatic ecosystems impacted by AgNP discharge. To evaluate the potential for AgNPs to alter benthic-pelagic coupling in aquatic ecosystems, tissue-stable isotope ratios of carbon and nitrogen from northern pike (Esox lucius) and yellow perch (Perca flavescens) were measured before, during, and after the addition of AgNPs to a whole-lake ecosystem, and compared to those collected from a nearby reference lake. A shift in carbon isotope ratios toward more negative values was observed in both P. flavescens and E. lucius collected from the lake where AgNPs were added, with no shift in similar magnitude observed in E. lucius from the reference lake. Consequently, Bayesian estimates of benthic energy consumed decreased by 32% for P. flavescens and by 40% for E. lucius collected after AgNP additions relative to pre-addition estimates, greater in magnitude or opposite in direction of trends observed in our reference lake. Analyses suggest no changes in fish nitrogen isotope ratios related to AgNP additions. We hypothesize that the observed reduction in littoral energy use of fish reported here is a response to AgNP settling in littoral benthic habitats-the main habitat in lakes supporting periphyton-as AgNP has been shown elsewhere to significantly reduce the rates of periphyton production. Further, our study highlights the need to broaden the scope of risk assessments for AgNPs and other emerging contaminants prone to settling to consider habitat-specific impacts on resource utilization by organisms after their release into aquatic ecosystems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".