Effects of ligand-bound silver on <i>Ceriodaphnia dubia</i>
Bibliographic record
Abstract
In aqueous media, ionic silver concentrations are low and transport occurs in the colloidal phase. In the aquatic environment, silver forms 1:1 complexes with thiol-containing compounds such as cysteine and glutathione. In order to quantitatively characterize the risk associated with silver in aquatic ecosystems, the bioavailabilities and toxicities of silver cysteinate and silver glutathionate were characterized. Static renewal bioassays were conducted with Ceriodaphnia dubia to estimate chronic toxicity, using mortality and reproduction as endpoints. Silver nitrate was the most lethal compound, with a median lethal concentration (8-d LC50) of 0.32 microg Ag/L (95% confidence interval [CI] = 0.19-0.54). The 48-h LC50 for AgNO3 was 0.5 microg/L and did not change significantly through 8 d. The presence of food in the bioassay did not change the 48-h LC50 for AgNO3. Silver glutathionate (AgGSH) and silver cysteinate (AgCys) induced less mortality during the 8-d bioassay. Silver cysteinate appeared to have the greatest effect on fecundity, with a no-observable-effect concentration (NOEC) less than 0.001 microg/L. Silver nitrate and AgGSH had lowest-observable-effect concentration (LOEC) values (nominal concentrations) of 0.01 and 0.6 microg/L, respectively. Results indicate that the ligand-bound silver in these laboratory studies is bioavailable and impairs reproduction of C. dubia at low aqueous concentrations.
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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".