The effects of large roughness elements on the in-stream transport and retention of polystyrene microplastics
Bibliographic record
Abstract
The mechanisms controlling transport and retention of microplastics (MPs) in riverine systems are not understood well. We investigated the impact of large roughness elements (LREs) on in-stream transport and retention of the ubiquitous polystyrene-microplastics (PS-MPs). Scaled experiments were conducted with and without LREs under various shear Reynolds numbers (Re*) in an ecohydraulics flume. Our results, for the first time, demonstrated a clear dependence of the MPs' velocity on Re* in LREs-dominated channel. Two distinct regimes and thresholds were identified: lower Re* (≤ 15,000) regime corresponding to higher velocities of MPs ([Formula: see text]> 0.45), and higher Re* (> 15,000) to lower [Formula: see text]< 0.45). The presence and higher density of LREs increased Re*, decreased [Formula: see text], and enhanced the PS-MPs capture. The LREs-generated turbulence kinetic energy (TKE) was found to be a good predictor of PS-MPs transport and retention rates, indicating the effectiveness of LREs in retaining PS-MPs in streams and rivers.
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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".