Response of Crustacean Zooplankton and Benthic Macroinvertebrate Communities to Selenium Additions in a Boreal Lake
Bibliographic record
Abstract
Selenium (Se) is a contaminant of concern in Canada mainly due to its teratogenic effects on fish and birds. However, few studies have assessed the effects of Se on invertebrates in a field setting. The objective of this experiment was to assess potential community-level impacts of Se additions on zooplankton and benthic macroinvertebrates in a boreal lake ecosystem. From June to August 2018, Se (as selenite) was added to six limnocorrals in Lake 239 at the International Institute for Sustainable Development-Experimental Lakes Area, Northwestern Ontario, Canada, to achieve mean measured aqueous concentrations of 0.4, 0.8, 1.6, 3.4, 5.6 and 7.9 µg Se/L, with three untreated limnocorrals serving as controls (background Se = 0.08-0.09 µg/L). Periphyton, phytoplankton, and invertebrates (zooplankton and benthos) were monitored for 63 days. Zooplankton community composition shifted as a function of Se exposure, with Cladocera biomass and density decreasing with increasing Se concentrations. Similarly, cumulative abundance and biomass of Heptageniidae decreased with increasing Se treatment throughout the experimental period. The present study demonstrated that Se can have impacts on aquatic invertebrates at environmentally relevant exposure levels, and that future ecological risk assessments should consider the impacts of Se on both vertebrates and invertebrates. Environ Toxicol Chem 2022;41:95-107. © 2021 SETAC.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".