Spatial and temporal trends in the fate of silver nanoparticles in a whole-lake addition study
Bibliographic record
Abstract
Studies of the fate and toxicity of nanoparticles, including nanosilver (AgNPs), have been primarily conducted using bench scale studies over relatively short periods of time. To better understand the fate of AgNPs in natural aquatic ecosystems over longer time scales and ecological settings, we released suspensions of AgNPs (30-50 nm, capped with polyvinylpyrrolidone) into a boreal lake at the Experimental Lakes Area in Canada. Approximately 9 kg of silver was added from a shoreline point source from June to October 2014, which resulted in total Ag (TAg) concentrations of about 10 μg L-1 or less. In addition, dissolved Ag concentrations (DAg) were typically very low. Using single particle inductively coupled plasma mass spectrometry (sp-ICP-MS) analysis of grab samples, we found that the nanoparticles typically ranged in the 40-60 nm size class and were widely distributed throughout the lake, while larger aggregates (i.e. >100 nm) were infrequently detected. The highest occurrence of aggregates was found near the addition site; however, size distributions did not vary significantly among spatial locations or time suggesting rapid dispersal upon entry into the lake. Lake stratification at the thermocline was not a barrier to mobility of the AgNPs, as the particles were also detected in the hypolimnion. Environmental factors influenced Ag size distributions over sampling locations and time. Total dissolved phosphorus, bacterioplankton chlorophyll-a, and sampling time strongly correlated with aggregation and dissolution dynamics. AgNPs thus appear to be relatively mobile and persistent over the growing season in lake ecosystems.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".