Geochemistry of arsenic in uranium mill tailings, Saskatchewan, Canada
Bibliographic record
Abstract
The Rabbit Lake in-pit tailings body consisted of alternating layers of ice, frozen tailings and unfrozen tailings which varied in consistency from a slurry to a firm silty sand. The tailings solids are predominately composed of quartz (16 to 36%), calcium sulphate (0.3 to 54%) and illite (3 and 14%). Arsenic and Ni concentrations in the tailings showed similar patterns with depth, which were strongly related to historical changes in As and Ni concentrations in the mill feed. Mineralogy of the ore bodies indicated that As and Ni in the mill feed occurred primarily as 1:1 molar ratio arsenides such as niccolite and gersdorffite. SEM analysis suggested that solubilized arsenic is precipitated as Ca, Fe and Ni arsenates during the neutralization process. Dissolved arsenic concentrations in rive monitoring wells installed within the tailing body ranged from 9.6 to 71 mg/L. Sequential extraction analyses of tailings samples showed that As above 34 in depth was primarily associated with amorphous iron and metal hydroxides while the As below 34 m depth was primarily amorphous calcium arsenate precipitates. The high Ca/As ratio during tailings neutralization would likely preferentially precipitate Ca4(OH)2(AsO4)2:4H2O. Geochemical modeling suggested that the pore fluid calcium arsenate equilibrium As concentrations would range between 13 and 81 mg/L. The predicted pH and speciation of arsenic in the filter sand was dependent on the redox conditions (oxidizing or reducing) assigned to the regional groundwater. Reducing conditions in the regional groundwater cause As, the dominant species in the tailings, to be reduced to As 34 as arsenic diffuses from the tailings into the filter sand. Under reducing conditions, iron as Fe2+ in the filter sand is oxidized to Fe3+ as the sulphate (S6+) present in the tailings diffuses into the filter sand and is reduced to sulphide (S2). The pH in the tailings will decrease as the high concentrations of protons (lower pH) in the filter sand diffuse into the tailings. As the solubility of calcium arsenate minerals present in the tailings are pH dependent, the decrease in pH in the tailings causes an increase in solubility of the calcium arsenate minerals resulting in the dissolution of calcium arsenate minerals.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".