Potential Impacts of Tailings and Tailings Cover Fertilization on Arsenic Mobility in Surface and Ground Waters
Bibliographic record
Abstract
A number of mining sites worldwide, particularly gold mines, have tailings management facilities (TMFs) that contain high levels of arsenic. Current closed mine site regulatory agencies tend to prefer revegetation of TMFs as part of the mandated reclamation activities. At many sites, often in polar regions, vegetation is difficult to establish either directly on the tailings or on the coarse-rock covers due to nutrient poor soils, phytotoxicity problems, and/or a less than optimum climate. Addition of phosphorus-based fertilizers to the tailings and/or cover material is commonly considered in order to promote the revegetation process and – ideally – allow the site owners to discharge their closure duties as rapidly as possible. However, due to the similar geochemistry of arsenic and phosphorus oxyanion species, this type of mine closure strategy may have unintended consequences regarding arsenic mobility on and off the site. This document reviews the current state-of-the-art regarding mobilization of arsenic by phosphate ions, and identifies relevant risks and opportunities of using this information to better manage closed mine sites.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".