Accumulation of Silver in Yellow Perch (<i>Perca flavescens</i>) and Northern Pike (<i>Esox lucius</i>) From a Lake Dosed with Nanosilver
Bibliographic record
Abstract
A total of 15 kg of silver nanoparticles (AgNPs) was added continuously over two ice-free field seasons to a boreal lake (i.e., Lake 222) at the IISD Experimental Lakes Area in Canada. We monitored the accumulation of silver (Ag) in the tissues of yellow perch ( Perca flavescens) and northern pike ( Esox lucius) exposed to the AgNPs under environmentally relevant conditions. The greatest accumulation was observed in the liver tissues of pike, and a single pike sampled in the second year of additions had the highest concentration observed in liver of 5.1 micrograms per gram of wet weight. However, the Ag concentrations in gill and muscle tissue of both pike and perch did not exceed 0.35 micrograms per gram of wet weight. Following additions of AgNP, the Ag residues in fish tissues declined, with a half-life of Ag in pike liver of 119 days. Monitoring using passive sampling devices and single-particle inductively coupled plasma mass spectrometry during the AgNP addition phase confirmed that Ag nanoparticles were present in the water column and that estimated mean concentrations of Ag increased over time to a maximum of 11.5 μg/L. These data indicate that both a forage fish and a piscivorous fish accumulated Ag in a natural lake ecosystem dosed with AgNPs, leading to Ag concentrations in some tissues of the piscivorous species that were 3 orders of magnitude greater than the concentrations in the water.
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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) | 0.000 | 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 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".