Whole-lake silver nanoparticles addition promotes phosphorus and silver excretion by yellow perch (Perca flavescens)
Bibliographic record
Abstract
Fish excretion supports primary production by supplying essential nutrients. However, our understanding of how contaminants affect fish nutrient excretion rates and their persistence in aquatic environments is limited. We investigated the effect of chronic exposure to silver nanoparticles (AgNPs) under environmentally relevant conditions on yellow perch (Perca flavescens) nutrients and silver (Ag) excretion. Fifteen kg of AgNPs were added over two ice-free seasons to a lake at the IISD-Experimental Lakes Area in Canada. We measured the nitrogen, phosphorus, and Ag excretion rates and ratios by perch pre-, during, and post the AgNPs addition phases in both the experimental and the reference lakes over a 10-year period. We found that in Year 2 of AgNPs addition, exposed perch P excretion rates and P:Ag excretion ratios increased (P < 0.05). However, our empirical P excretion rates in both lakes diverged from model-based predictions of P excretion rates for the tested fish size range, leaving the reasons for the increase in P excretion rates speculative. Nonetheless, Ag release by perch indicates that fish may contribute to legacy Ag contamination in aquatic ecosystems, thereby extending Ag exposure and uptake by aquatic organisms.
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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".