Soil contamination by microplastics in a small French agricultural watershed
Bibliographic record
Abstract
Plastics have many beneficial uses in agriculture, but their degradation contributes to diffuse microplastic (MP) contamination of soils, affecting soil structure, biota, and downstream water quality. Their sources, numerous for agricultural soils, are often difficult to identify locally. Despite an awareness of these threats, few global studies outside China have characterized these contaminants in agricultural soil. Among these studies, few have evaluated the fate of microplastics while taking into account the various agricultural practices. This field study focused on characterizing microplastics in French agricultural soil with various land uses in a small watershed, both at the surface and down to a depth of 60 cm, the typical tillage depth. Microplastics concentrations in greenhouse surface soils using plastic mulching were found up to 1.1 · 10 4 MPs/kg, which is significantly higher than other land uses studied (i.e., agricultural crop fields, stream banks, and forests). Further, microplastic concentrations were found to decrease by > 80% from the top 20 cm of soil to the below 20 – 60 cm of soil. These findings highlight the need to minimize microplastic sources in agricultural soil, in particular, from greenhouse films and plastic mulching, through policy and materials that reduce UV degradation.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".