Evaluation of a risk management strategy for wetland sediment contaminated with gold mine tailings using bloodworms (Chironomus dilutus)
Bibliographic record
Abstract
Wetlands in Nova Scotia (NS) have been contaminated by mercury (Hg) and geogenic arsenic (As), the waste products of gold mining during the 1800s.These elements have since remained in the environment, bioaccumulating in benthic species and transferring through higher trophic levels.Here, we evaluated using a reactive amendment (R) composed of zerovalent iron (ZVI), supported by a protective capping (PC) of silica sand, ZVI, bentonite, and zeolite as a risk management strategy for impacted wetlands.We examined the treatment's ability to reduce contaminant toxicity to the freshwater larval invertebrate Chironomus dilutus, commonly known as bloodworms, in a laboratory experiment.Additionally, in preparation for a field mesocosm test assessing the in situ success of the treatment at Muddy Pond in NS, a pilot cage test was conducted to assess potential cage effects on chironomid survival and identify an appropriate cage mesh size among 200 µm, 243 µm and 300 µm that would allow chironomids with the most sediment exposure while preventing them from escaping.There was 100% survival in the control cages, indicating that the cages were not a considerable source of mortality in chironomids.The highest survival in contaminated sediment was for cages with 243 µm mesh.The toxicity test confirmed that total water Hg and As concentrations overlying the contaminated sediment were significantly reduced by at least 71% and 99% respectively, when treated with both R and PC.Chironomid survival significantly increased from 45% in the contaminated sediment to 90% when treated, while growth significantly increased by 36.5% and Hg bioaccumulation decreased by 42%.Our study indicates that this risk management strategy is safe for chironomids and can successfully reduce sediment toxicity to this invertebrate.
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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 | 0.001 |
| 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 | 0.001 | 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".