Microplastics in Agricultural Soils: A New Challenge Not Only for Agro-environmental Policy?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it