Effects of Soil Composition and Mineralogy on the Bioaccessibility of Arsenic from Tailings and Soil in Gold Mine Districts of Nova Scotia
Bibliographic record
Abstract
Bioaccessibility tests and mineralogical analyses were performed on arsenic-contaminated tailings and soils from gold mine districts of Nova Scotia, Canada, to examine the links between soil composition, mineralogy, and arsenic bioaccessibility. Arsenic bioaccessibility ranges from 0.1% to 49%. A weak correlation was observed between total and bioaccessible arsenic concentrations, and the arsenic bioaccessibility was not correlated with other elements. Bulk X-ray absorption near-edge structure analysis shows arsenic in these near-surface samples is mainly in the pentavalent form, indicating that most of the arsenopyrite (As(1-)) originally present in the tailings and soils has been oxidized during weathering reactions. Detailed mineralogical analyses of individual samples have identified up to seven arsenic species, the relative proportions of which appear to affect arsenic bioaccessibility. The highest arsenic bioaccessibility (up to 49%) is associated with the presence of calcium-iron arsenate. Samples containing arsenic predominantly as arsenopyrite or scorodite have the lowest bioaccessibility (<1%). Other arsenic species identified (predominantly amorphous iron arsenates and arsenic-bearing iron(oxy)hydroxides) are associated with intermediate bioaccessibility (1 to 10%). The presence of a more soluble arsenic phase, even at low concentrations, results in increased arsenic bioaccessibility from the mixed arsenic phases associated with tailings and mine-impacted soils.
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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 | 0.001 |
| 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 | 0.001 |
| 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".