Investigation of the Sheltering Effects on the Mobilization of Microplastics in Open-Channel Flow
Bibliographic record
Abstract
Microplastics (MPs) can be sheltered by bed grains, resulting in a higher threshold of mobilization compared with those moving on a smooth bed. Only one formula considering the sheltering effects has been proposed for the critical shear stress ( τ c ) of MP mobilization by including the densities and sizes of MPs and bed grains, but it is inaccurate for MPs of certain materials such as PS and shows limitations in understanding the sheltering effects. Additionally, no method exists for the critical depth-averaged velocity ( U c ). In this study, experiments were conducted measuring both τ c and U c of MPs (made of PA, PVC, and PET) deposited on rough beds of different roughnesses, with a focus on the sheltering effects. A power law relationship between τ c and the density and size of MPs was found, and a new formula for estimating τ c was proposed for MPs, which reduced the errors by approximately 40% compared with that of the previous formula (27.8%). To explicitly quantify the sheltering effects in MP mobilizations, hiddenness ( ΔZ ), exposure ( ΔH ), and longitudinal exposure ( ΔX ) were introduced, all of which were observed to follow a normal distribution. A semiempirical method for U c was then proposed incorporating the new measures, which reduced the errors by around 70% compared to the methods developed for estimating the U c values of sediments.
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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) | 0.000 | 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".