Mineralogical Controls on Aluminum and Magnesium in\nUranium Mill Tailings: Key Lake, Saskatchewan, Canada
Bibliographic record
Abstract
The mineralogy and evolution of Al and Mg in U mill tailings are\npoorly understood. Elemental analyses (ICP-MS) of both solid and aqueous\nphases show that precipitation of large masses of secondary Al and\nMg mineral phases occurs throughout the raffinate neutralization process\n(pH 1–11) at the Key Lake U mill, Saskatchewan, Canada. Data\nfrom a suite of analytical methods (ICP-MS, EMPA, laboratory- and\nsynchrotron-based XRD, ATR-IR, Raman, TEM, EDX, ED) and equilibrium\nthermodynamic modeling showed that nanoparticle-sized, spongy, porous,\nMg–Al hydrotalcite is the dominant mineralogical control on\nAl and Mg in the neutralized raffinate (pH ≥ 6.7). The presence\nof this secondary Mg–Al hydrotalcite in mineral samples of\nboth fresh and 15-year-old tailings indicates that the Mg–Al\nhydrotalcite is geochemically stable, even after >16 years in the\noxic tailings body. Data shows an association between the Mg–Al\nhydrotalcite and both As and Ni and point to this Mg–Al hydrotalcite\nexerting a mineralogical control on the solubility of these contaminants.
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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) | 0.000 | 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) | 0.051 | 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 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".