Water and sediment chemistry influences on mercury bioaccumulation in freshwater invertebrates from two lakes in Kejimkujik National Park, Nova Scotia
Bibliographic record
Abstract
Mercury is a trace metal and a toxic environmental pollutant that can be deposited in remote ecosystems and result in adverse effects on the heath of organisms. Higher mercury concentrations near the bottom of the food web have implications for significant mercury transfer to higher trophic organisms. Caddisfly larvae and mayfly naiads, lake water, and benthic sediment were collected from two lakes and one wetland catchment in Kejimkujik National Park, Nova Scotia. Methylmercury(MeHg) was analyzed in all samples using gas chromatography-atomic fluorescence spectroscopy (AFS). Across all sampling sites, pH was significantly negatively correlated with MeHg concentration in mayflies. Water samples from Big Dam East (BDE) and Big Dam West (BDW), sediment samples from BDW, and invertebrate samples from BDE from the wetland catchment (Thomas Meadow Brook; TMB) were significantly higher in MeHg concentration than the lake locations. Mayfly MeHg concentrations were positively correlated with water MeHg concentrations. Caddisfly MeHg concentrations were not significantly correlated with water MeHg concentrations, however, results suggest that caddisfly MeHg might be correlated with sediment MeHg concentrations (BDW and TMB R=-0.23 and -0.21, respectively). These results indicate that caddisflies may be more influenced by sediment characteristics, while mayflies may be more influenced by water characteristics. Analysis of MeHg bioaccumulation in aquatic invertebrates and its relationship with water and sediment characteristics is important to understand relative accumulation between invertebrates, and more research into MeHg bioaccumulation at the base of food webs is necessary to better understand food web transfer.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".