The Effect of Microplastics with Different Types, Particle Sizes, and Concentrations on the Germination of Non-Heading Chinese Cabbage Seed
Bibliographic record
Abstract
Microplastics (MPs) are a new type of pollutant widely distributed in the environment. The ecological risks caused by MPs are becoming increasingly serious, especially in cultivated land where pollution is more likely to accumulate. In this paper, the effects of different types, particle sizes, and concentrations of MPs on the seed germination of non-heading Chinese cabbage were analyzed to reveal their potential mechanisms. Five types of MPs, polypropylene (PP), polyethylene (PE), polyvinyl chloride (PVC), polyethylene terephthalate (PET), and polystyrene (PS), were used for correlation analysis. The results showed that the effect of PVC and PET on seed germination was greater than that of PP, PS, and PE. PVC and PP promoted the growth of germinated seeds, while PET and PS showed a certain degree of inhibition. The effect of MPs with a particle size of 6.5–150 μm on seed germination was obvious. Low-concentration MPs (<1 g/L) had a weak inhibitory effect on seed germination. When the concentration was 1 g/L, 75 μm-PP, 75 μm-PVC, and 150 μm-PS promoted the growth of germinated seeds, while 48 μm PET showed inhibition. At high concentration, PP and PS inhibited amylase activity. In general, MPs’ effects showed significant differences according to different types, particle sizes, and concentrations.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| 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 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".