Polyurethane microplastics and associated tris(chloropropyl)phosphate additives both affect development in larval fathead minnow <i>Pimephales promelas</i>
Bibliographic record
Abstract
Microplastics (< 5 mm) are a diverse class of contaminants ranging in morphology, polymer type, and chemical cocktail. Microplastic toxicity can be driven by one or a combination of these characteristics. Most studies, however, evaluate the physical effect of the most commercially available polymers. By disregarding other polymers with high consumption and/or production rates, and the chemical constituents of plastics, we fail to have a holistic understanding of the mechanisms of toxicity. Polyurethane is understudied in terms of effects testing yet considered one of the most hazardous polymers due to its chemical composition. Polyurethane is a high production polymer and is found in common consumer products ranging from packaging to spray foam insulation. To better understand the physico-chemical effects of polyurethane and a common additive in polyurethane products, we exposed larval fathead minnows for 28 days to polyurethane without chemical additives (i.e., plastic treatment), chemical leachate from polyurethane containing chemical additives (i.e., tris(chloropropyl)phosphate [TCPP]; i.e., chemical treatment) and polyurethane with chemical additives (i.e., plastic with chemical treatment) in a fully factorial experiment. We observed significant decreases in growth at 12 days posthatch (dph) in the plastic, chemical, and plastic with chemical treatments, suggesting a physical and chemical driver of toxicity. At 28 dph, we did not observe significant differences in growth, suggesting individuals can recover. We also observed concentrations of ΣTCPPs in fathead minnow exposed to the plastic with chemical treatment and the chemical only treatment, demonstrating TCPP uptake in exposed individuals. Combined, our data suggests the importance of both the physical and chemical components of microplastics when assessing effects, and thus emphasizing the need to evaluate the effects of microplastics in a multidimensional way.
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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 | 0.001 |
| 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".