Biofilm-induced sinking of buoyant microplastics in a freshwater environment
Bibliographic record
Abstract
The degree of microplastic dispersal and retention in lakes and oceans critically depends upon the microplastic particle’s density, which can change over time due to microbial growth (biofilm). This experiment tests the mechanism by which initially buoyant microplastics can be lost from the surface layers of a lake and become deposited in sediments. While buoyant microplastics do initially float in water, the growth of biofilm denser than water on the microplastic surface leads to an increase in particle density as a whole. This increase in density results in slower rise velocities of biofouled particles when they are mixed into the water column, and can even lead to sinking of biofouled particles. Both slower rise velocities and particle sinking would increase microplastic residence time in the water body. Through ex-situ experiments on irregularly-shaped polypropylene microplastic granules in an emulated lake environment under overcast light levels, we have found that biofouling alone is sufficient to increase microplastic particle density and lead to sinking for small particles (~125-212 µm) in 18 days and larger particles (1000-2000 µm) in 50 days. These differences in settling onset time would likely lead to size-fractionation of particle sedimentation, where smaller particles are deposited closer to their sources relative to larger particles. Using the measured values of biofilm-induced sinking rates of larger microplastics (1000-2000 µm) and lake residence times, we can describe the fraction of microplastics expected to become deposited after they enter lakes. Our results on terminal velocity change inherent to biofouling provide new information for microplastic transport modelling.
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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 | 0.000 |
| 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".