Macroplastic surface characteristics change during wind abrasion
Bibliographic record
Abstract
Abstract Mechanical abrasion is an important wind driven process which can degrade plastic litter on sandy beaches, desert environments and in agricultural settings. Wind-driven particle impacts can cause surface roughening and chemical changes and eventually complete fragmentation in high stress environments. Aeolian abrasion has been considered in the context of microplastics (< 5 mm) which can be easily mobilised by wind. However, macroplastic (> 5 mm) abrasion has primarily been confined to engineering studies using high air velocities (> 25 m s −1 ) and large abraders (> 6 mm) which generate greater impact forces than observed in the natural environment. Using laboratory abrasion experiments, we demonstrate that the surface microtextures and surface chemistry of three different types of plastic are substantially altered during the processes of aeolian abrasion at impact particle velocities of 0.6 m s −1 . After ten days of continuous abrasion with four different erodents the macroplastic surfaces developed textures resulting from micro-cutting, denting, flaking, micro-pitting and surface flattening. The prevalence of each surface texture was dependent upon the angularity of the erodent and the type of plastic. In all cases, polymer surface chemical compositions became more complex due to embedding of shattered abrasive and the replacment of carbon with oxygen and silica.
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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) | 0.002 | 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".