Polyester microfibers delay growth of cherry tomato (<em>Solanum lycopersicum var. cerasiforme</em>) throughout the lifecycle
Bibliographic record
Abstract
Agroecosystems are increasingly recognized as major basins for terrestrial microplastics. Many agricultural practices have led to high loading of plastic mulch films, synthetic microfibers, and other microplastics onto fields. There is demonstrated ability for microplastics to influence soil properties and plant productivity, but these effects are highly variable by species, soil, and life stage. Here, we conduct a lifecycle assessment of how polyester microfibers, a dominant biosolid contaminant, affect the development of cherry tomato (Solanum lycopersicum var. cerasiforme) in peaty growing medium. We distinguish the importance of physical and chemical characteristics of microfibers by comparing plants in soil containing microfibers at 0.5% soil weight, or soil watered with leachate isolated from microfibers. We find that polyester microfibers reduce emergence success by 11%, delay flowering and ripening time by several days, and lead to a reduction in biomass in adult plants. However, we observe no effect of chemical additives from microfibers on plant development. We also note a decrease in soil water holding capacity from microfibers. Overall, we conclude that physical microfiber properties or physical/chemical interactions are the likely drivers of biological effects. These findings emphasize that microfibers have impacts beyond mineral soils and consequences at every stage of plant development.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".