The need for ecologically realistic studies on the health effects of microplastics
Bibliographic record
Abstract
Abstract Plastic pollution is now so widespread that microplastics are consistently detected in every biological sample surveyed for their presence. Despite their pervasiveness, very little is known about the effects of microplastics on the health of terrestrial species. While emerging studies are showing that microplastics represent a potentially serious threat to animal health, data have been limited to in vivo studies on laboratory rodents that were force fed plastics. The extent to which these studies are representative of the conditions that animals and humans might actually experience in the real world is largely unknown. Here, we review the peer-reviewed literature in order to understand how the concentrations and types of microplastics being administered in lab studies compare to those found in terrestrial soils. We found that lab studies have heretofore fed rodents microplastics at concentrations that were hundreds of thousands of times greater than they would be exposed to in nature. Furthermore, health effects have been studied for only 10% of the microplastic polymers that are known to occur in soils. The plastic pollution crisis is arguably one of the most pressing ecological and public health issues of our time, yet existing lab-based research on the health effects of terrestrial microplastics does not reflect the conditions that free-ranging animals are actually experiencing. Going forward, performing more true-to-life research will be of the utmost importance to understand the impacts of microplastics and maintain the public’s faith in the scientific process.
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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 | 0.036 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".