The Effects of Large Roughness Elements on the In-stream Transport and Retention of Polystyrene Microplastics
Bibliographic record
Abstract
Abstract The dominant mechanisms controlling the concentration and transport of microplastics (MPs) in riverine systems are not fully understood yet. Polystyrene (PS) is a highly abundant MP in streams and rivers. This study investigated the impact of large roughness elements (LREs) on in-stream transport and retention patterns of polystyrene-microplastics (PS-MPs). Experiments were conducted with and without LREs under a range of shear Reynolds numbers (𝑅𝑒∗) in an eco-hydraulics flume. We found a clear dependence of MPs’ velocity on 𝑅𝑒∗ in LREs-dominated channel. The results also revealed that the LREs-generated turbulence kinetic energy (TKE) can be a good predictor of PS-MPs transport and retention rate. This indicates the effectiveness of TKE in retaining PS-MPs as they travel through streams and rivers. The presence of LREs increases the PS-MPs capture and decreases their velocity of transport. This suggests that PS-MPs retention can be increased by increasing the LRE density in reverine systems.
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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 | 0.000 |
| 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".