Impact of Redox Conditions on Arsenic Mobilization from Tailings in a Wetland with Neutral Drainage
Bibliographic record
Abstract
More than 80 years of silver mining in the Cobalt area (Ontario, Canada) has led to widespread contamination of water with arsenic. The objective of this study was to determine the impact of changes in redox conditions on the stability of As in samples collected from a tailings wetland in the historic mining camp. Dissolved metal concentrations were monitored while tailings samples (approximately 1300 mg of As kg(-1), pH 7.4) were subjected to 30 days of reduction. Reoxidation of the samples was accomplished by air drying. The As oxidation states in the original, reduced, and reoxidized samples were determined using X-ray absorption spectroscopy (XAS). Arsenic speciation was affected by changes in redox conditions, resulting in rapid mobilization of As during reduction. Glucose input had a significant impact on the dissolution and speciation of As, suggesting that the As transformation was microbially mediated. When carbon was not limiting, the combination of reducing conditions and lower pH favored the formation of As(-I) species.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".