Evaluation of a customized reactive nanoscale-zero-valent iron and zeolite thin capping blend for enhancing natural recovery of wetlands impacted by contaminated legacy gold mine tailings
Bibliographic record
Abstract
Abstract Legacy gold mine tailings from the 1800’s in Nova Scotia, Canada have elevated mercury (Hg) and arsenic (As) concentrations. Tailings, were slurried into wetlands without treatment. Over a century later, those impacted wetlands are still at risk and innovative in-situ treatment approaches to support natural biological and chemical recovery are needed. Here we report results of our proof-of-concept laboratory study with a customized reactive thin layer capping to limit mobility, bioaccumulation and toxicity of Hg and As in wetland sediment impacted by legacy tailings. The customized reactive amendment is a blend of NANOFER STAR nanoscale zero valent iron (nZVI) and fine-grained zeolite (clinoptilolite) inserted either below, or within a thin cap (silica sand, bentonite and zeolite) and placed over contaminated wetland sediments in beakers. Due to the high concentrations of Hg and As in sediments, invertebrates (Hyalella azteca, Daphnia magna and Caridina multidente) exposed to untreated wetland sediment exhibited high mortality and bioaccumulation of Hg. The reactive capping applications improved the survival of H. azteca and D. magna similar to the survival rates seen in our clean control sediment. Bioaccumulation of Hg was also reduced in C. multidente exposed to the treated sediment compared to the untreated sediment. Furthermore, total [Hg] and [As] in the overlaying water of treated contaminated sediments were reduced by 88% and 99% respectively. Our proof-of-concept testing of this reactive capping blend shows potential for managing and supporting natural recovery of wetlands impacted by historical gold-mine tailings.
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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".