The effects of microplastics on freshwater phytoplankton and zooplankton communities in a boreal lake
Bibliographic record
Abstract
Our understanding of the potential impacts of microplastics (MPs) on freshwater ecosystems is limited. There is evidence that high MP concentrations can negatively impact phytoplankton and zooplankton under laboratory conditions, but community level effects under natural conditions are unknown. Two large scale in-situ mesocosm (limnocorral) experiments were conducted at the International Institute for Sustainable Development Experimental Lakes Area (IISD-ELA) in northwestern Ontario, Canada to assess the responses of phytoplankton and zooplankton communities to MP additions. In the first experiment, a mixture of common polymers (polyethylene; PE, polystyrene; PS, and polyethylene terephthalate; PET) were added in equal contributions to limnocorrals in a range of environmentally relevant nominal concentrations (0-29,240 MPs/L for all polymers together). In the second experiment, the same mixture of polymers was added to limnocorrals at a total concentration of 29,240 MPs/L each with and without chemical additives and these were compared to controls with no MPs to distinguish if there was a physical or chemical mechanism for MP toxicity. Phytoplankton biomass was not affected in either experiment, but there may be some potential for MPs to affect chlorophyll a production and community composition over time. There was weak evidence that zooplankton abundance and biomass were stimulated by MPs in both experiments, and copepod reproduction was slightly reduced. Overall, there was little evidence of significant negative impacts of MPs on the plankton communities.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".