Data from: No effect of realistic concentrations of polyester microplastic fibers on freshwater zooplankton communities
Bibliographic record
Abstract
Zooplankton are a conduit of energy from autotrophic phytoplankton to higher trophic levels, and they can be a primary point of entry of microplastics into the aquatic food chain. Investigating how zooplankton communities are affected by microplastic pollution is thus a key step towards understanding ecosystem-level effects of these global and ubiquitous contaminants. Although the number of studies investigating the biological effects of microplastics has grown exponentially in the last decade, the majority have used controlled laboratory experiments to quantify the impacts of microplastics on individual species. Given that all organisms live in multi-species communities in nature, here we use an outdoor 1130L mesocosm experiment to investigate the effects of microplastic exposure on natural assemblages of zooplankton. We endeavored to simulate an environmentally relevant exposure scenario by manually creating ~270,000 0.015mm x 1-1.5mm polyester fibers and inoculating mesocosms with zero, low (10 particles/L) or high (50 particles/L) concentrations. We recorded zooplankton abundance and community composition three times throughout the 12-week study. We found no effect of microplastics on zooplankton abundance, Shannon diversity, or Pielou’s evenness. NMDS plots also revealed no effects of microplastics on zooplankton community composition. Our study provides a necessary and realistic baseline upon which future studies can build. Because numerous other stressors faced by zooplankton (e.g. food limitation, eutrophication, warming temperatures, pesticides) are likely to exacerbate the effects of microplastics, we caution against concluding that polyester microfibers will always have no effect on zooplankton communities. Instead, we encourage future studies to investigate the triple threats of habitat degradation, climate warming, and microplastic pollution on zooplankton community health.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.032 | 0.007 |
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".