Food availability is a critical factor in microplastic toxicity testing using <i>Daphnia magna</i>
Bibliographic record
Abstract
Microplastics are ubiquitous in the environment and can have toxic effects on organisms. The effects of microplastics can include food dilution. This occurs when an animal feels full after consuming particles but does not gain nutrition from them. This satiety signal might limit further feeding, resulting in malnutrition. Environmental concentrations of microplastics and food are relevant to the risk of food dilution. The ratio of ingested microplastics to food by volume should determine the degree of food dilution and other toxicity pathways. To examine the possibility of a relationship between food availability and the effects of microplastics on animal health, we used a fully factorial design experiment exposing Daphnia magna to three concentrations of microplastic fragments (none, low, high). We exposed Daphnia to a mixture of three microplastic polymers (polyethylene, polyethylene terephthalate, polystyrene) and three levels of food availability (low, medium, high). We found that microplastics negatively affected survival only in the high exposure treatment and that food availability improved survival across all treatments. Higher survival of Daphnia in the high-microplastics, high-food treatment compared with the high-microplastics, medium and low-food treatments suggests that the two factors interact additively. Food availability also positively influenced reproduction and growth, whereas microplastic exposure did not. Future studies and the interpretation of past work should closely consider the relationship between food availability and microplastics, as the effect of microplastics on survival appears to be mediated by the abundance of food. Exposures to the same microplastic concentration across different levels of food will produce different experimental outcomes, which will affect the thresholds determined by risk assessments.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".