Where Is All the Plastic? How Microplastic Partitions across Environmental Compartments within a Large Pelagic In-Lake Mesocosm
Bibliographic record
Abstract
How microplastics transit within aquatic ecosystems and partition among environmental compartments is not fully understood. To increase understanding, we added microplastic fragments ranging in buoyancy (positive: polyethylene (PE), neutral: polystyrene (PS), negative: polyethylene terephthalate (PET)) and size (∼30 to 1400 μm) to surface waters of closed-bottom, in-lake mesocosms (10 m diameter, 2 m depth). To assess residence time, we measured microplastics in surface waters and the water column over a 9-week period. To measure fate, we measured microplastics in the surface water, water column, bottom detritus, and biota (biofilm on the walls, zooplankton, fish) at 9 weeks. The residence times of microplastics were longer at the surface than in the water column, with less dense and smaller particles having the longest residence times. After 9 weeks, nearly all microplastics were on the bottom, with only 3% on the surface, 0.4% in the water column, 2% in biofilm, and <0.01% in zooplankton and fish. The surface water and biofilm on the walls were larger reservoirs than the water column, suggesting that surface microlayers and biofilm on hard substrates are important, yet overlooked, reservoirs of microplastics in aquatic ecosystems. Results inform future hypotheses relevant to monitoring programs and 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 | 0.000 | 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".