Standardized toxicity testing may underestimate ecotoxicity: Environmentally relevant food rations increase the toxicity of silver nanoparticles to <i>Daphnia</i>
Bibliographic record
Abstract
Daphnia in the natural environment experience fluctuations in algal food supply, with periods when algal populations bloom and seasons when Daphnia have very little algal food. Standardized chronic toxicity tests, used for ecological risk assessment, dictate that Daphnia must be fed up to 400 times more food than they would experience in the natural environment (outside of algal blooms) for a toxicity test to be valid. This disconnect can lead to underestimating the toxicity of a contaminant. We followed the growth, reproduction, and survival of Daphnia exposed to 75 and 200 µg/L silver nanoparticles (AgNPs) at 4 food rations for up to 99 d and found that AgNP exposure at low, environmentally relevant food rations increased the toxicity of AgNPs. Exposure to AgNP at low food rations decreased the survival and/or reproduction of individuals, with potential consequences for Daphnia populations (based on calculated specific population growth rates). We also found tentative evidence that a sublethal concentration of AgNPs (75 µg/L) caused Daphnia to alter energy allocation away from reproduction and toward survival and growth. The present findings emphasize the need to consider resource availability, and not just exposure, in the environment when estimating the effect of a toxicant. Environ Toxicol Chem 2017;36:3008-3018. © 2017 SETAC.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".