The Impacts of Ethanol and Freeze–Thaw Cycling on Arsenic Mobility in a Contaminated Boreal Wetland
Bibliographic record
Abstract
Pyrite-bearing waste rock from legacy gold mines is a source of elevated arsenic, sulfate, and iron in the surrounding environments due to leaching. Waste rock in environments that experience cold winters is of particular concern because freeze–thaw cycling may mobilize elements through degradation and release of organic matter and accelerated mineral weathering. In boreal zones, wetlands are common recipients of mine-waste run-off, and microbial processes in wetland soil may promote the retention of mobilized elements, such as arsenic. We investigated the impacts of freeze–thaw cycling and ethanol amendment on soil from an arsenic-contaminated wetland in anoxic microcosms. Ethanol-amended microcosms exhibited enhanced microbial sulfate reduction, leading to sulfide precipitation and increased retention of arsenic in the soil. Sequential extraction studies indicated a shift of arsenic into more stable sulfide-bound fractions. The addition of ethanol significantly increased the growth of Geobacter spp. and other select sulfate-reducing bacteria. Freeze–thaw cycling increased dissolved arsenic over short time periods only and had no detectable impacts on microbial activity. These findings suggest that the use of ethanol as an amendment to wetlands during spring thaw may enhance arsenic sequestration in mining-impacted soils and may provide a viable remediation strategy for cold-climate environments, where seasonal freeze–thaw cycling could otherwise contribute to arsenic mobilization.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".