Microplastic effects on soil aggregation in sterilized and non-sterilized soils
Bibliographic record
Abstract
The perceived impact of soil microplastics on soil aggregation is primarily attributed to their potential toxicity toward soil microorganisms. However, only a limited number of studies have undertaken comprehensive controlled experiments involving sterilized soils to substantiate this notion. The present study embarked on soil incubation experiments encompassing both non-sterilized soils and soils subjected to oven-heating sterilization to investigate the ramifications of polyethylene (PE) and polypropylene (PP) microplastics, characterized by mesh sizes of 30, 150, and 1000, on both the water-stability and mechanical stability of soil aggregates. The presence of microplastics decreased aggregation stability in Tianjin soils (on average -48.89% and -81.61% for 0.5-1 and 1-2 mm aggregates, respectively). The impact of microplastics was notably more evident in the non-sterilized soils. Microplastics also demonstrated the capacity to modify aggregate properties such as surface roughness. This study indicates the pivotal role played by interactions between soil microplastics and microorganisms on soil aggregation, but microplastics hold the potential to influence soil aggregation through non-biogenic pathways.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".