Lake Phytoplankton Assemblage Altered by Irregularly Shaped PLA Body Wash Microplastics but Not by PS Calibration Beads
Bibliographic record
Abstract
Microplastics are an emerging environmental pollutant, whose global ubiquity is becoming increasingly evident. Conventional wastewater treatment does not completely remove them, and there are growing concerns about microplastics in source water and post-treatment drinking water. Microplastics have been reported to alter the development, physiology, and behavior of various aquatic organisms; however, limited knowledge exists on their effect on natural phytoplankton communities. Many studies also use uniformly spherical plastic beads, while most scrub particles in consumer products and secondary microplastics in the environment have various shapes and sizes. We tested the effects of two types of microplastics, 50 µm polystyrene (PS) calibration beads and polylactic acid (PLA) plastic body wash scrub particles, and one type of plant-derived body wash scrub particle on a natural phytoplankton assemblage through a 7-day incubation experiment in a temperate, mesotrophic lake. The calibration beads and the plant-derived particles generally did not alter the taxonomic composition of the phytoplankton in the mesocosms, while the PLA body wash microplastics eliminated cryptophytes (p < 0.001) and increased chrysophytes (p = 0.041). Our findings demonstrate differential effects of irregularly shaped PLA body wash microplastics vs. PS calibration beads on lake phytoplankters and empirically support potential bottom-up alteration of the aquatic food web by secondary microplastics.
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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".