Digestible Fluorescent Coatings for Cumulative Quantification of Microplastic Ingestion
Bibliographic record
Abstract
The ubiquitous presence of microplastics in the environment makes it imperative to understand their effects. In particular, we must understand exposure, i.e., how many microplastics are ingested by organisms. This has proved difficult because counting microplastics in an organism’s gut content provides only a snapshot in time. Here, we show a method that uses a digestible fluorescent coating (DFC) to quantify cumulative microplastic ingestion. Our method enables precise and automated enumeration of cumulative microplastic ingestion with the flexibility to track different microplastic types and sizes with distinct fluorescent tracers. We confirm the coating is not acutely toxic and is not preferentially ingested by several invertebrate species. This method provides a unique and reliable approach to quantify cumulative microplastic ingestion in laboratory exposure studies, and can be used to advance our understanding of the impact of microplastics to wildlife.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".