Microplastics in Agricultural Soils: A New Challenge Not Only for Agro-environmental Policy?
Bibliographic record
Abstract
Microplastic pollution has recently gained the attention of the public media, politics and research. Microplastics (i.e., plastic particles less than 5mm in size) have been identified as a global environmental threat for terrestrial and aquatic ecosystems and human health. Agriculture is assumed to be both victim and polluter. Agricultural soils receive microplastic immissions from tire wear and fragmented macroplastic that enters the environment through littering. Furthermore, farmers who fertilize their arable land with sewage sludge and compost unintentionally apply the microplastic particles contained in these biosolids. On the other hand, agricultural soils may emit microplastics into aquatic environments. Because of this ambivalent position as both victim and polluter, the information on microplastic pollution is of current interest for agricultural production and might become a relevant topic for agro-environmental policies in the future. Our research aims to quantify the microplastic immissions into agricultural soils and emissions from agricultural soils into aquatic systems. We use different analysis approaches and interdisciplinary modelling to address these aims for two case studies in Germany. Because research in microplastics is a relatively new concern, we combine different methodological approaches in a complementary way.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".