Long-term exposure to polyethylene restructures the multi-kingdom soil microbiota in maize fields
Bibliographic record
Abstract
Soil contamination from polyethylene (PE) has emerged as a new global concern, yet its long-term legacy effects on soil microbiota remain poorly understood. Here, we conduct an eight-year field experiment to investigate how PE residues influence microbiota assembly across multiple microbial kingdoms (bacteria, fungi, and protists), and the consequent effects on soil antibiotic resistome. Our results reveal that bacterial communities are more stable and resilient than fungal and protistan communities in response to PE exposure. Bacterial assembly is predominantly shaped by deterministic processes under PE exposure, unlike the more stochastic patterns observed in the other domains. This bacterial deterministic assembly coincides with enhanced microbial biodegradation potential, evidenced by increased abundance of carbon-cycling functional genes in the plastisphere. In parallel, antibiotic resistance genes (ARGs) are found to be more prevalent in the plastisphere under PE exposure. While bacterial hosts play a dominant role in ARGs dissemination, fungal and protistan taxa also contribute through broader inter-kingdom ecological interactions. Together, these findings highlight the critical importance of considering multi-kingdom microbiota assembly when assessing the environmental risks of plastic pollution.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".