Characterizing and Quantifying Controls on Arsenic Solubility over a pH Range of 1−11 in a Uranium Mill-Scale Experiment
Bibliographic record
Abstract
A mill-scale hydrometallurgical experiment (2700 m3 of effluent treated/day) was conducted for three months at the Rabbit Lake uranium mine site located in northern Saskatchewan, Canada, to determine the controls on the solubility of dissolved arsenic over a pH range of 1-11 and to develop a thermodynamic database for the dominant mineralogical controls on arsenic in the mill and the resulting mill tailings. The arsenic concentrations in the mill ranged from 526 mg/L at pH 1.0 (initial) to 1.34 mg/L at pH 10.8 (final discharge). Geochemical modeling of the chemistry data shows that arsenic solubility is controlled by the formation of scorodite (FeAsO4-2H2O) from pH 2.4 to pH 3.1, with 99.8% of dissolved arsenic precipitated as scorodite. Model results show that scorodite is unstable (releasing arsenic back in to solution) above pH 3.1 and arsenic adsorption to the surface of 2-line ferrihydrite is the dominant controlling factor in the solubility of arsenic from pH 3.2 to pH 11.0, with 99.8% of dissolved arsenic removed from solution via this mechanism. Finally, model results show -0.2% of the total dissolved arsenic adsorbs to the surface of amorphous aluminum hydroxide from pH 5.0 to pH 8.0. Minor alterations to the thermodynamic properties of arsenite and arsenate adsorption to 2-line ferrihydrite allowed the fit between measured mill-scale and modeled concentrations for the pH range of 3.2-11.0 to be optimized.
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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 | 0.001 | 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) | 0.001 | 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".