A Combined Effect of Mixed Multi-Microplastic Types on Growth and Yield of Tomato
Bibliographic record
Abstract
Microplastics (MPs) are plastic particles ranging from 1000 to 5000 µm in diameter, posing a growing environmental and health risk. Composting is an excellent way to add nutrient-rich humus to the soil to boost plant development, but it also pollutes agricultural soil with MPs. Previous research has shown that MPs can threaten plant development, production, and quality, hence they must be studied. This study examined how a mixture of three MP types—polyethene (PE), polystyrene (PS), and polypropene (PP)—affected greenhouse tomato plant development. MP types were spiked at 1% w/w (MPs/soil) in tomato pots, whereas non-spiked growth medium was the control. Statistical analysis was conducted using an analysis of variance (ANOVA) and Tukey’s test (95% confidence) to compare treatments and controls. Soil spiked with MPs increased chlorophyll content (SPAD), transpiration rate, photosynthetic rate, and stomata conductance by 5.16%, 16.71%, 25.81%, and 20.75%, respectively, compared to the control but decreased sub-stomata CO2 concentration by 3.23%. However, MPs did not significantly affect tomato plant morpho-physiological features (p > 0.05). Biochemical analysis of tomato fruits showed significant (p < 0.05) reduction effects of MPs on carotenoid, total flavonoid, and sugar but increased protein, ascorbate, and peroxidase activity. However, there was no significant difference (p > 0.05) in the effects of the combined MPs on total phenolic content. These data imply that whereas MPs did not influence tomato plant physiological and morphological properties, tomato fruit biochemistry was reduced. This raise concerns that an increase in MPs in soils may reduce antioxidant content and negatively affect human health contributing to a decrease in food security.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".