Ecotoxicity of plastic leachates on aquatic plants: Multi-factor multi-effect meta-analysis
Bibliographic record
Abstract
• Ecotoxicity of plastic leachates on aquatic plants is analyzed by proposed PLEM. • Plastic leachates negatively affect aquatic plants' growth and photosynthesis. • Reveal impacts of plastic characteristics, leaching conditions, and plant species. • Plastic leachate toxicity and particle effects were distinguished. • Standardized guidelines for preparing plastic leachate solutions are essential. Despite heightened awareness of plastic contamination, a comprehensive understanding of the ecotoxicity of plastic leachates remains challenging due to discrepancies in previous findings and complexities in the effects of myriad factors. Herein, we proposed a multi-factor multi-effect plastic-leachate ecotoxicology meta-analysis approach (PLEM) to elucidate the ecotoxicity of plastic leachates on aquatic plants. To distinguish the leachate toxicity from the general effects of leachates and plastic particles, the previous studies on the effects of leachate stricto sensu (i.e., without particles) were exclusively encompassed. A total of 890 data points explored in 18 previous articles were systematically analyzed. Our findings revealed that plastic leachates negatively affected aquatic plants' growth (31 %) and photosynthesis (13 %). These toxic effects were influenced by multifaced factors including plastic characteristics, leaching conditions, and plant species. Polyvinyl chloride leachates exhibited the highest toxicity among different polymers. Marine species showed greater susceptibility than freshwater species. Surprisingly, leachates from centimeter-sized plastics exhibit higher toxicity than those from nanometer, micrometer, and millimeter-sized plastics. These findings underscore the toxicity of plastic leachates on aquatic plants should be more systematically assessed using standardized laboratory methods and considering multi-factors. This study offers a valuable insight into the toxic mechanism of plastic leachates and plastic contamination.
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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 | 0.021 | 0.026 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.060 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".