Geochemistry of arsenic in uranium mill tailings, Saskatchewan, Canada
Bibliographic record
Abstract
The Rabbit Lake uranium mine &pit tailings body consists of alternating layers of ice.frozen tdings and untiozen tailings.The tailings sotids are predominately composed of quartz (I6 to 36 @A).calcium sulphate (03 to 54 wt%).iIlite (3 and 14 wt%) As-Ni sulphide 2 wt% and iron 2 wt%.Arsenic and Ni concentrations in the tailings showed similar panems with depth, which were strongly related to historical changes in As and Ni c o n c e n ~o n s 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.EMP analysis suggested that solubilid arsenic is precipitated as Ca Fe. and Ni arsenates during the neutralization process.Dissolved As concentrations in five monitoring wells installed within the tailing body ranged h m 9.6 to 71 mgL.Pore fluids in the wells had a pH between 9.3 and 10.3 and measured Eh between +58 and +213 mV.Sequential extraction analyses of tailings samples showed that As above 34 m depth was primarily associated w i t h amorphous iron and metal hydroxides whiie the As below 34 m depth was associated with Ca.Iikely as amorphous calcium arsenate precipitates.The change in the dominant As solid phases at this depth was attributed to the differences in the molar ratio of Fe to As in the mill feed.Below 34 m it was <2 whereas above 34 m it was >4.The high CdAs ratio during tahgs neutralization would Iikely pkferentially precipitate Ci14(OH)z(As04)~:4H~O.Geochemical modeling.using PHREEQC.suggested that if the pore fluids were brought to equilibrium with hydrated calcium arsenate.the long-term dissolved As concenuations would range between 13 and 81 m a .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 gmundwter c a w HA SO^^ the dominant species in the railings.to be reduced to ~2 ~~0 3 ' -as difhses h m the railings into the sand Under dphate reducing conditions, imn as ~e * in the filter and is oxidhed to Fe(m) species as the sulphate (S(VI)) present in the tailings diffuses into the filter sand and is reduced to sulphide (S(-Q).The pH in the tailings will decxwe as the high concentrations of protons in the ater sand d B k into the tailings.As the soIubiIity of calcium arsenate minerals present in the tailings are pH dependent the decrease in pH in the t a h g causes an increase in solubiIiq of the dcium arsenate minerals resulting m the dissolution of caIcium armate minetals.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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 teacher head, 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".