Influence of microplastics on small-scale soil surface roughness and implications for wind transport of microplastic particles
Bibliographic record
Abstract
Microplastics are an anthropogenic contaminant widely recognized for their effect on marine and freshwater systems, but their terrestrial effects remain less well studied. The inclusion of microplastics in soils has the potential to affect a range of different soil properties, including bulk density, hydraulic conductivity and aggregation. Soil properties affect the susceptibility of soils to wind erosion, and it is therefore likely that where the quantity of microplastics present in soils is sufficient to change soil properties, it may also change the response of soils to wind erosion. This paper quantifies whether the presence of microplastics in sediments affects the development of small-scale soil surface roughness (SSR) properties during wind erosion, and whether there are any relationships between indices of SSR and microplastic flux due to wind erosion. Two contrasting substrates (well-sorted sand and poorly sorted soil) and two types of microplastic (polyethylene beads and polyester fibres) are used. SSR is quantified using geostatistically derived indicators calculated from high-resolution laser scans of the soil surface with and without microplastics, and before and after wind erosion simulated using a wind tunnel. Our results reveal the relative size of the microplastic to the mineral sediment is key to controlling microplastic flux.This article is part of the Theo Murphy meeting issue 'Sedimentology of plastics: state of the art and future directions'.
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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 | 0.001 |
| 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".